demo_data/lectures/Lecture-01-18.04.2023/English.vtt

WEBVTT

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That easy to say this is a good translation
and this is a bad translation.

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How can we evaluate?

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We will put an emphasis on machine translation
because that is currently the state of the

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art.

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But we are now focused on the details of neural
networks where we are describing the basic

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ideas and how to use the info machine translation.

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This is not a neural network course.

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If you have some background in Neo Networks,
that is of course of an advantage, but it should

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not be a challenge.

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If you have not done the details, we'll shortly
cover the background and the main ideas.

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How can we use them for for?

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Machine translation: We will starve the first
two, three lectures with some like more traditional

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approaches how they work because they still
give some good intuition, some good ideas.

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And they help us to understand where our systems
might be better.

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And yeah, we have an innocence on really what
do we need to do to build a strong system.

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And then we have a part on experience where
it's about how to build the systems and how

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to apply it.

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For additional reading materials, so we have
the slides on the website.

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There is also links to papers which cover
the topic of the lecture.

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If You'd Like to Study Additional Books.

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Think the most relevant is this machine translation
from Philip Kurnan, which gives an introduction

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about machine translation.

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But this lecture is, of course, not a one
to one like we don't go through the book, but

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it covers related topics.

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Is a previous version of that statistical
machine translation focusing on that part,

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and we cover some of that part rather than
all.

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If you want to have more basics about natural
language processing, this might be helpful.

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In addition, there is an online course on
machine translation which we also develop here

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at which is available.

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Input where you're, of course, free to use
that I might give you some other type of presentation

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of the lecture important is.

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It's, of course, a lot shorter and book doesn't
cover all the topics which you're covering

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in the lecture.

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So, of course, for the exam everything which
was in the lecture is important.

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This covers like the first half where don't
know exactly the first X lectures.

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Feel free to have a look at that.

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It's shorter.

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Maybe there's some of you interesting to have
very short videos or after the lecture single

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this topic I didn't understand want to repeat.

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Then this might be helpful, but it's important
that there is more content in the lecture.

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The exam will be minutes and oral exam and
just make an appointment and then.

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If you think this is a really cool topic,
want to hear more.

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There's two similars, one on advanced topics
in machine translation.

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Which is every Thursday and there is one which
was already on Monday.

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But if you're interested in speech translation
to contact us and there, I think,.

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Then there are other lectures, one more learning
by Professor Vival, and for us some of you

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have already.

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Lecture, which is related but of discovering
more general natural language processing than

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will be again available in.

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Winter semester, and then we are concentrating
on the task of machine translation and mighty.

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Yeah, and also there's an automatic speech
emission problem.

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And this is a bit what we are planning to
talk about in this semester.

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Today we have a general.

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Then on Thursday we are doing a bit of a different
lecture and that's about the linguistic.

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It may be quite different from what you're
more computer scientist, what you've done there,

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but don't worry.

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We're coming in a very basic thing that I
think it's important if you're dealing with

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natural language to have a bit of an understanding
of what language isn't.

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Maybe I've learned about that in high school,
but also for you this I guess some years ago.

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And so it's a bit of yeah, it better understand
also what other challenges there.

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And especially since we are all dealing with
our mother time, it may be English, but there

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is a lot of interesting phenomena which would
not occur in these two languages.

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And therefore we'll also look a bit into what
are things which might happen in other languages.

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If we want to build machine translation, of
course we want to build machine Translation

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for many.

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Then we will see a lot of these machine learning
based how to get the data and process the data

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next week.

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And then we'll have one lecture about statistical
machine translation, which was the approach

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for twenty years.

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And then maybe surprisingly very early we'll
talk about evaluation and this is because evaluation

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is really essential for machine translation
and it's very challenging.

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To decide if machine translation output is
good or bad is really challenging.

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If you see another translation for a machine
to decide is not as difficult and even for

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a machine translation output and ask them to
rate, you'll get three different answers: And

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so it's worse to investigate it, and of course
it's also important to have that at the beginning

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because if we're later talking about some techniques,
it will be always saying this technique is

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better by x percent or so.

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And we'll also have a practical good course
of this.

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Then we're going to build language models
which are in point to translation models.

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After the half you have a basic understanding
of what and basic machine translation.

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And then on the second part of the lecture
we will cover more advanced topics.

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What are the challenging?

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One challenge is, of course, about additional
resources about data.

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So the question is how can we get more data
or better data and their different ways of

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doing?

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Our thralling data will look into our building
systems which not translate between one language

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but which translate between fifteen languages
and youth knowledge and share knowledge between

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the language so that for each pair they need
less data.

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And then we'll have something about efficiency.

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That is, of course, with more and more complex
models.

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Because then nobody can afford to do that,
so how can you build really efficient things?

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Who also like energy is getting more expensive
so it's even more important to build systems.

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We're Looking to Biases So.

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That is a machine translation quite interesting
because some information are represented different

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in languages.

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So if you think about German, there is always
clear or not.

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But in a lot of situations, it's clear if
you talk about to teach her about.

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Another Person If It's Male or Female.

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From English to German you don't have this
information, so how do you generate that and

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what systems?

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Will just assume things and we'll see that
exactly this is happening, so in order to address

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these challenges and try to reduce.

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The main adaptation is what I said that beginning
systems are good at the task they are trained.

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But how can we adapt them to new task?

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Document level is doing more context and we
have two lectures about speech translation,

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so mostly before we are translating.

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Are now translating audio things.

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We have just additional challenges and these
we will address.

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So to the motivation, why should you work
on the theme translation and why should you

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put effort?

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So we want or we are living in a more global
society.

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You have now the chance to communicate with
people.

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And the danger of course is that languages
are dying, and more and more languages are

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going away.

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I think at least that some opportunity in
order to keep more languages is that we have

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technology solutions which help you to speak
in your language and still communicate with

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people who speak another language.

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And on the one hand there is the need and
more and more people want to speak in some

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other languages.

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For example, Iceland was really keen on getting
Icelandic into commercial systems and they

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even provided data and so on because they wanted
that their language is spoken longer and not

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just people switching.

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So there's even like yeah, they were spending
for promoting this language in order to have

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all these digital tools available for languages
which are not spoken by so many people.

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So it's questionable and it's not completely
clear technology always provides.

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If we think about machine translation, there
are different use cases in which you can use

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that.

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And this has some characteristics: So typically
in this case it is where machine translation

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was used first anybody.

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Because most youth outlets around the world
report at least some of the same events, like

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was probably covered around the world in a
lot of different languages.

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That is one point yes, so the training gator
is there.

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That's definitely a good point here and then.

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Yes, there was my regional idea.

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The motivation program was a bit different
by you, but it's a good point.

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So on the one end you'll understand maybe
not perfect English.

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Also, it's for his personal use, so you're
using machine translation for you use.

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It's not as important that this is really
perfect written text, but you're more interested

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in understanding.

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Maybe it's more clearer if you think about
the other situation where it's about dissimination

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that means producing text in another language.

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So just imagine you have a website or you
have a restaurant and you want to offer your

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menu.

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And in this case maybe you want to have a
higher quality because in some of your.

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You're presenting something of yourself and
you want to have good quality.

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Just remember you're writing a letter and
if you're translating your letter then you

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don't want to have it full of mistakes because
it's somehow a bad, bad oppression but if it's

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assimilation it's about you getting the information.

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So here you want your disciplination, you're
producing texts for another language.

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And then you have the disadvantage that you
maybe want to have a higher quality.

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Therefore, typically there is less amount,
so normally you're getting more information

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than you're producing.

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Then of course there is a dynamic scenario
where there is some type of interaction and

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the one thing which is interesting about the
dialogue scenario is there is: So if you're

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translating a website you have all the data
available but in a dialogue scenario you.

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And we'll see that in speech recognition this
is a big challenge.

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Just to mention German where in German the
work is often more at the end, so each harmony.

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Know that you want to generate the English
sentence.

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Now you need to know if you cancel this registration
to produce a second word.

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So you have to either guess or do something
in order to provide the translation before

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the translation is already.

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The question, of course, is in the new world.

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I mean, of course, we can, on the one hand,
say we don't want to have English, but the

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question is do we really need that many languages
and how many are here at the moment?

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Does anybody have an idea how many languages
are spoken in the world?

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This is already the first big challenge.

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What a language is and what no language is
is already difficult, and then maybe one point

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people have to argue first about written language
or spoken languages.

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For written languages I think that number
is still too low, but for a spoken language

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people normally think: So you see that it's
really a lot of languages which will be difficult

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to all happen.

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And these are just like you see Europe where
there's relatively few languages.

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You already have quite a lot of languages,
even walls and countries.

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Of course sometimes you share the language,
but then you have Briton or Gillesian vest

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where you have languages in a country.

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And yeah, of course, there's the question:
When does it start to be a language?

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And when is it more like a dialect?

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So is Catalan?

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Is Swiss German a known language?

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Or is it the same?

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So then, of course, it's are like Czech and
Slovakian.

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I know heard that people can understand each
other so they can just continue talking and

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understand by some of their own language and.

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Of course, it's partly also like about your
own nationality, so I think some people said

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creation.

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But think for a lot of people you shouldn't
say that they are part of being creation language.

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But you see therefore that it is not completely
clear that there is no hardwater between this

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and the new language, and this is a different
one.

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And of course it's getting more fluent when
you talk about scientific things.

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I guess sometimes it's no longer clear if
it's German or English because we start to

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use a lot of English terms in there.

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So of course there's interesting mixes which
will talk.

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So should everybody just speak English, and
these numbers are a bit older, have to admit:

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However, I don't think they're completely different
now and it says like how many people know in

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Europe can speak English for countries where
English is not the mothertown or for people.

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In some countries like smaller ones, for smaller
countries you have quite high numbers.

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However, there are many countries where you
have like twenty to thirty percent of the population,

0:20:13.979 --> 0:20:16.370
only being able to speak English.

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So if we would only do everything only in
English, we would exclude half the population

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of Europe.

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And therefore providing translations is very
important and therefore, for example, the European

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Parliament puts a really large amount of money
into doing translation.

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So that's why you can speak in your mother
too in the European Parliament.

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Everybody like everyone elected there can
speak in there and they were translated to

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all the other languages and it's a huge effort
and so the question is can we do better with

0:20:52.247 --> 0:20:52.838
machine.

0:20:53.493 --> 0:20:58.362
And for other countries things are even more.

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They may be not worse, difficult, but they
are even more challenging.

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So there's even more diversity of languages
and it might be even more important to do machines.

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If you see how many people speak French, Portuguese
or English, it's relatively few compared to

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the population.

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So think that this should be around millions
would understand you, but all the others wouldn't.

0:21:49.289 --> 0:21:54.877
So it seems to be very important to provide
some taebo translation.

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It's a quite big industry as a European Union.

0:21:58.740 --> 0:22:05.643
This is already also quite long ago, but it
won't get less spent like in that year.

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One point three billion on translation.

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So it might be very helpful to have tools
in order to provide them, and as said, not

0:22:21.315 --> 0:22:26.267
all directions might be important.

0:22:26.426 --> 0:22:35.059
Is even not possible for students, so in the
European Parliament they don't have all combinations

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of the different.

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And language is so if they want to translate
from Maltese to Estonian or so.

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And maybe they have a translator for that,
but there are some directions which don't have

0:22:47.361 --> 0:22:47.692
that.

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Then they handle directly, but they would
translate first to French, German or or English,

0:22:52.706 --> 0:22:57.721
and then there would be a second translator
getting the translation and really translating

0:22:57.721 --> 0:22:59.154
to your Italian language.

0:22:59.299 --> 0:23:06.351
And it's not always English, so they are really
selecting what is most helpful.

0:23:06.351 --> 0:23:13.931
But you see that even in this small setup,
with this large amount of effort in there,

0:23:13.931 --> 0:23:17.545
there's not enough ability to translate.

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And of course this was text.

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Then you have a lot of other things where
you want to, for example, do speech translation.

0:23:26.538 --> 0:23:31.744
There is a lot of conferences which currently
are all held in English, which of course might

0:23:31.744 --> 0:23:35.831
also not be the best solution if you've gone
to some of the conferences.

0:23:36.176 --> 0:23:45.964
You might have heard some accented speech
where people speak a language that is very

0:23:45.964 --> 0:23:49.304
different from their mother.

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Might be difficult to understand.

0:23:52.212 --> 0:23:59.123
We're currently having an effort for example
by ACL, which is the conference organized in

0:23:59.123 --> 0:24:06.112
this field to provide these translations into
ten hour languages so that also students who

0:24:06.112 --> 0:24:06.803
are not.

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That familiar English is able to read the
papers and watch the present case.

0:24:16.416 --> 0:24:25.243
So the question is what can you do here and
one interesting solution which we'll cover

0:24:25.243 --> 0:24:26.968
in this lecture?

0:24:27.087 --> 0:24:38.112
This always comes with a question: is it will
it replace the human?

0:24:38.112 --> 0:24:40.382
And yes, the.

0:24:40.300 --> 0:24:49.300
Idea, but the question doesn't really happen
and I'm any skeptical about that.

0:24:49.300 --> 0:24:52.946
So currently we are not seeing.

0:24:53.713 --> 0:24:55.807
So much more effort needed.

0:24:55.807 --> 0:25:00.294
Of course, machine translation is now used
as some type of.

0:25:01.901 --> 0:25:11.785
If you think about in the European Parliament,
they will have some humans doing their translation

0:25:11.785 --> 0:25:18.060
because: If you think about the chancel of
Germany trembling somewhere and quite sure

0:25:18.060 --> 0:25:18.784
you want,.

0:25:19.179 --> 0:25:31.805
And so it's more like we are augmenting the
possibilities to have more possibilities to

0:25:31.805 --> 0:25:37.400
provide translation and travel around.

0:25:39.499 --> 0:25:53.650
How can this technology help so machine translation
is one way of dealing with?

0:25:54.474 --> 0:26:01.144
Of course, there is other tasks which do even
without machine translation.

0:26:01.144 --> 0:26:04.613
Just think about summarize my lecture.

0:26:04.965 --> 0:26:08.019
Approaches doing that what they call end to
end.

0:26:08.019 --> 0:26:11.635
So you just put an English text and get a
German summary.

0:26:11.635 --> 0:26:17.058
However, a good baseline and an important
thing is to either first lecture into German

0:26:17.058 --> 0:26:22.544
and then do a summary art, first do a summary
in English and then translation language.

0:26:23.223 --> 0:26:28.764
Translation is very important in order to
different application scenarios.

0:26:28.764 --> 0:26:33.861
We have that dissemination dialogue but also
information extraction.

0:26:33.861 --> 0:26:39.993
So if you want to do like get information
not only from English websites but from.

0:26:40.300 --> 0:26:42.427
Very different websites.

0:26:42.427 --> 0:26:46.171
It's helpful to have this type of solution.

0:26:50.550 --> 0:26:52.772
Yeah, what can you translate?

0:26:52.772 --> 0:26:59.660
Of course, we will focus on text, as I said
for most of them, because it's about translation

0:26:59.660 --> 0:27:06.178
and anything first translates to text, and
then change to text, and then we can do text

0:27:06.178 --> 0:27:07.141
translation.

0:27:09.189 --> 0:27:19.599
And text is not equals text, so we can do
translation that is some of the most common.

0:27:19.499 --> 0:27:27.559
Is working on translation, so just imagine
you are developing your new.

0:27:27.947 --> 0:27:34.628
Nowadays you don't want to have to only be
available in English or German books in as

0:27:34.628 --> 0:27:40.998
many languages as possible, and if you use
the standard tools it's not that easy.

0:27:41.141 --> 0:27:50.666
We have a different type of domain and there
again we have very few contexts.

0:27:50.666 --> 0:27:56.823
Normally we translate: To pick up an app you
have the menu and there's like safe.

0:27:57.577 --> 0:28:02.535
And then you only have safe.

0:28:02.535 --> 0:28:14.845
How should translate safe should it be written
or should it be spicing?

0:28:16.856 --> 0:28:24.407
Then, of course, if you have like files, it
might be that you have meta data to transport.

0:28:26.466 --> 0:28:27.137
Novels.

0:28:27.137 --> 0:28:32.501
Some work on that, but yeah, that's always
a typical criticism.

0:28:32.501 --> 0:28:36.440
You'll never be able to translate Shakespeare.

0:28:36.656 --> 0:28:43.684
Think this is somehow the last use case of
machine translation.

0:28:43.684 --> 0:28:47.637
For a translation of books there's.

0:28:47.847 --> 0:28:57.047
But the nice thing about machine translation
is that it can translate to things which are

0:28:57.047 --> 0:29:05.327
boring, so think about translating some bureaucrative
forms or some regulations.

0:29:05.565 --> 0:29:11.302
This is normally not very interesting, it's
very repetitive, so their automation works

0:29:11.302 --> 0:29:11.697
well.

0:29:11.931 --> 0:29:17.519
Of course, there is also translations on Paibos
images.

0:29:17.519 --> 0:29:24.604
I guess you point your camera to an object
where it translates things.

0:29:25.005 --> 0:29:43.178
And we'll cover that at the end, as said,
the speech translation.

0:29:43.663 --> 0:29:46.795
So you can't provide the translation of the
lecture.

0:29:46.795 --> 0:29:50.518
If I'm five slides further then you would
see the translation.

0:29:50.518 --> 0:29:52.291
It might not be very helpful.

0:29:54.794 --> 0:29:57.062
We are not speaking as we are written.

0:29:57.062 --> 0:29:59.097
It's again like a domain mismatch.

0:29:59.359 --> 0:30:10.161
So typically the sentences are not full sentences
and I'm saying this is not the right way to

0:30:10.161 --> 0:30:19.354
praise it and if you just read what was written
it might be hard to understand.

0:30:23.803 --> 0:30:36.590
We are focusing on the first application scenario
that is fully out of management.

0:30:37.177 --> 0:30:46.373
Of course, there are quite interesting application
scenarios for other things where it should

0:30:46.373 --> 0:30:47.645
be referred.

0:30:47.867 --> 0:30:49.695
Where it's no longer going to be.

0:30:49.695 --> 0:30:52.436
We have this tool and it works, but it's a
market.

0:30:52.436 --> 0:30:57.381
We have the machine translation system and
the human translator, and they somehow cooperate

0:30:57.381 --> 0:30:59.853
and try to be as fast as possible in doing
a.

0:31:00.380 --> 0:31:12.844
The easiest idea there would be the first
point you take the machine translation.

0:31:13.553 --> 0:31:17.297
That sometimes farther might not be the best
way of suing it.

0:31:17.357 --> 0:31:25.308
Any ideas or what else you could do, then
maybe the machine could aid the human and say

0:31:25.308 --> 0:31:27.838
I'm sure about this author.

0:31:28.368 --> 0:31:32.319
Yeah, very interesting, very good.

0:31:32.319 --> 0:31:42.252
Of course, the dangerous thing there is you
asking something from a machine translation

0:31:42.252 --> 0:31:45.638
system where it's really bad.

0:31:45.845 --> 0:31:50.947
There is quality estimation that maybe it
will couple that in evaluation so in evaluation

0:31:50.947 --> 0:31:55.992
you know what is correct translation and you
have another output and you try to estimate

0:31:55.992 --> 0:31:57.409
how good is the quality.

0:31:57.409 --> 0:32:02.511
In quality estimation you don't have you only
have a source and time and good question is

0:32:02.511 --> 0:32:03.531
exactly this one.

0:32:03.531 --> 0:32:05.401
Is it a good translation or not?

0:32:05.665 --> 0:32:12.806
This might be easier because the system might
not know what translation is.

0:32:13.053 --> 0:32:23.445
Human is very good at that for machines that
are difficult, but of course that's an interesting

0:32:23.445 --> 0:32:24.853
application.

0:32:25.065 --> 0:32:32.483
Be more interactive so that you may be translating
if the human changes the fifth word.

0:32:32.483 --> 0:32:36.361
What does it mean for the remaining sentence?

0:32:36.361 --> 0:32:38.131
Do I need to change?

0:32:38.131 --> 0:32:43.948
There are also things like you don't have
to repeat the same errors.

0:32:47.767 --> 0:32:57.651
Hell our automated basemen, you only want
to correct at once and not at all positions.

0:33:00.000 --> 0:33:21.784
And then they ask, for example, so before
the translation is done they ask: I'm not directly

0:33:21.784 --> 0:33:23.324
aware of that.

0:33:23.324 --> 0:33:33.280
I think it's a good way of ending and I think
it's where, especially with more advanced dialogue

0:33:33.280 --> 0:33:34.717
strategy and.

0:33:35.275 --> 0:33:38.831
Currently think of most of the focus is like
at least determining.

0:33:39.299 --> 0:33:45.646
Don't have this information that is already
challenging, so there is quite some work on

0:33:45.646 --> 0:33:49.541
quality estimation that I'm missing your information.

0:33:49.789 --> 0:33:53.126
But is there something missing?

0:33:53.126 --> 0:33:59.904
It's really quite challenging and think that
is where currently.

0:34:00.260 --> 0:34:05.790
What is there is there is opportunities to
provide or there is models to directly provide

0:34:05.790 --> 0:34:06.527
additional?

0:34:06.786 --> 0:34:13.701
You can give them anything you have and provide
them.

0:34:13.701 --> 0:34:21.129
It's a similar situation if you're translating
to German.

0:34:21.641 --> 0:34:31.401
And it would just guess normally or do some
random guessing always means it's using some

0:34:31.401 --> 0:34:36.445
information which should not be really there.

0:34:36.776 --> 0:34:46.449
So then you can provide it with an additional
input or you should use formula or non formula.

0:34:47.747 --> 0:35:04.687
To know that this information is missing.

0:35:04.544 --> 0:35:19.504
Since you're not specifically modeling this,
it's likely that there is a gender difference

0:35:19.504 --> 0:35:21.805
in languages.

0:35:26.046 --> 0:35:39.966
One are we doing good search on machine translation,
so it's a very important part to ask in natural

0:35:39.966 --> 0:35:42.860
language processing.

0:35:43.283 --> 0:35:49.234
So of course you have a lot of computer science
thing in there and that's the backbone of.

0:35:49.569 --> 0:36:01.848
However, task and understanding you can also
get from information like computational linguistics,

0:36:01.848 --> 0:36:08.613
which tell you about what language it's good
to know.

0:36:08.989 --> 0:36:15.425
Doesn't mean that in a computer we have to
bottle it exactly the same, but for example

0:36:15.425 --> 0:36:22.453
to know that there is something like morphology,
which means how words are built, and that for

0:36:22.453 --> 0:36:24.746
some languages it's very easy.

0:36:24.746 --> 0:36:28.001
In English there is nearly no worth coming.

0:36:28.688 --> 0:36:35.557
Well in Germany you already start for soon
you have like different forms and so on.

0:36:36.316 --> 0:36:41.991
And for other languages, for finish, it's
even more complicated with Basque.

0:36:41.991 --> 0:36:44.498
I think for some words more than.

0:36:45.045 --> 0:36:52.098
So knowing this, of course, gives you some
advice.

0:36:52.098 --> 0:37:04.682
How do I look at that now because we'll see
in the basic treat each word as an individual?

0:37:06.106 --> 0:37:09.259
Of course there is a lot of interest also
prone from industry.

0:37:09.259 --> 0:37:10.860
There is a lot of applications.

0:37:11.191 --> 0:37:17.068
There's research groups at Google, Facebook,
and Amazon.

0:37:17.068 --> 0:37:26.349
So there's quite a lot of interest in providing
that for German and English it is solved.

0:37:26.546 --> 0:37:27.569
Annoucing it's hard.

0:37:27.569 --> 0:37:31.660
We're saying that not hard, but of course
we haven't acquired high quality in them.

0:37:32.212 --> 0:37:39.296
But there's currently really a large trend
in building other systems for low research

0:37:39.296 --> 0:37:40.202
languages.

0:37:40.480 --> 0:37:53.302
So there are tasks on last year's task on
translating from Native American languages:

0:37:53.193 --> 0:37:58.503
Don't know yet but but five other languages,
so how can you translate from them?

0:37:58.538 --> 0:38:05.074
Then you don't have like millions of sentences,
but you might have only the Bible or some more

0:38:05.074 --> 0:38:05.486
data.

0:38:05.486 --> 0:38:08.169
Then the question is, what can you do?

0:38:08.169 --> 0:38:09.958
And how good can you get?

0:38:14.794 --> 0:38:17.296
One thing is very important.

0:38:17.296 --> 0:38:25.751
Of course, in a lot of A I is to measure the
quality and what you can measure is quite important.

0:38:25.986 --> 0:38:37.213
So that's why for many years of regular there
is different evaluation campaigns where people

0:38:37.213 --> 0:38:38.178
submit.

0:38:39.419 --> 0:38:45.426
We're often part of the statistical machine
translation original, yet now I think it's

0:38:45.426 --> 0:38:51.019
a machine translation where it's mostly about
European languages and used texts.

0:38:51.051 --> 0:38:57.910
The International Workshop of Spoken Language
Translation, which is translation about lectures

0:38:57.910 --> 0:39:04.263
which we are co organizing, and there is a
bovia as I said building strong systems this

0:39:04.263 --> 0:39:04.696
time.

0:39:04.664 --> 0:39:11.295
This has established translating conference
presentations from English into ten different

0:39:11.295 --> 0:39:17.080
languages: And then, of course, you have to
deal with things like special vocabulary.

0:39:17.037 --> 0:39:23.984
You think about recurrent real networks are
terms like co-recurrent networks, convolutional

0:39:23.984 --> 0:39:24.740
networks.

0:39:25.545 --> 0:39:29.917
That might be more difficult to translate
and you also have to decide who I need to translate

0:39:29.917 --> 0:39:33.359
or should I keep it in English, and that's
not the same in each language.

0:39:33.873 --> 0:39:37.045
In German maybe mostly you keep it.

0:39:37.045 --> 0:39:44.622
I think in French people are typically like
wanting to translate as much as possible.

0:39:44.622 --> 0:39:52.200
These are then challenges and then, of course,
in Poland where it's also challenging.

0:39:53.153 --> 0:39:59.369
I think all of the speakers in the test that
are not native in your speakers, so you need

0:39:59.369 --> 0:40:05.655
to translate people with a German accent or
with a French accent or with a Japanese accent

0:40:05.655 --> 0:40:09.178
or an English accent, which poison has additional.

0:40:12.272 --> 0:40:21.279
Yes, so there is criticism always with new
technologies because people say will never

0:40:21.279 --> 0:40:23.688
translate Shakespeare.

0:40:24.204 --> 0:40:26.845
Partly agree with the second.

0:40:26.845 --> 0:40:34.682
Maybe it's not good at translating Shakespeare,
but there's many people working on that.

0:40:35.255 --> 0:40:38.039
Of course, the poison cookie is a challenge.

0:40:38.858 --> 0:40:44.946
The thing is here that the cookie chart that
you can't never be sure if the machine translation

0:40:44.946 --> 0:40:47.546
system doesn't really mistake somewhere.

0:40:47.546 --> 0:40:53.316
So if you can't be sure that there's no error
in there, how can you trust the translation?

0:40:55.275 --> 0:41:01.892
That is partly true, on the other hand, otherwise
you have to translate to a human translator

0:41:01.892 --> 0:41:06.116
and men who are sometimes overestimating human
performance.

0:41:06.746 --> 0:41:15.111
They are very good translators but under a
lot of pressure and not human translations.

0:41:15.715 --> 0:41:22.855
The question is: When can you trust it enough
anyway?

0:41:22.855 --> 0:41:28.540
You should be careful about trusting them.

0:41:31.011 --> 0:41:38.023
And I think some of them are too old now because
it has been shown that it is helpful to have

0:41:38.023 --> 0:41:41.082
some type of machine translation system.

0:41:41.082 --> 0:41:47.722
Of course, it is not buying the car, so typically
still a system is not working forever.

0:41:48.048 --> 0:41:56.147
If you want your dedicated system, which is
good for the task you are, they are typically

0:41:56.147 --> 0:41:57.947
not as generalized.

0:41:58.278 --> 0:42:07.414
That can translate news and chats, and I don't
know what.

0:42:07.414 --> 0:42:12.770
So typically if you want to show.

0:42:12.772 --> 0:42:18.796
It's not made for, it has not seen very well
and then you see a bad quality.

0:42:19.179 --> 0:42:27.139
But that's also like yeah, therefore you don't
build it.

0:42:27.139 --> 0:42:42.187
If you have a sports car and you are driving
off road you should: Yeah, you can also say

0:42:42.187 --> 0:42:49.180
the other way around trans machine translation
is already solved, and especially with more

0:42:49.180 --> 0:42:50.487
people think so.

0:42:50.750 --> 0:43:04.275
However, there is an impressive performance
of machine translation, but it's not stated

0:43:04.275 --> 0:43:06.119
of the art.

0:43:06.586 --> 0:43:11.811
And yeah, they're good for some domains and
some languages that are even like already.

0:43:12.572 --> 0:43:27.359
Have Microsoft has a very super human performance
claiming that their machine translated system.

0:43:27.467 --> 0:43:38.319
However, there was one domain use and some
language in Spanish where there is a huge amount

0:43:38.319 --> 0:43:45.042
of training data and you can build a very strong
system.

0:43:45.505 --> 0:43:48.605
And you even don't have to go to these extreme
cases.

0:43:48.688 --> 0:43:54.328
We have worked on Canada, which is a language
in India spoken.

0:43:54.328 --> 0:44:01.669
I think by also around eighty million people
so similar to to German that it has.

0:44:01.669 --> 0:44:07.757
The quality is significantly worse, it has
significantly less data.

0:44:08.108 --> 0:44:15.132
There are still quite a lot of languages where
the quality is not, where you want to have.

0:44:15.295 --> 0:44:17.971
Scaling this is not as easy at this thing.

0:44:17.971 --> 0:44:23.759
That's why we're also interested in multilingual
systems with the hope that we don't have to

0:44:23.759 --> 0:44:29.548
build a system for each possible combination,
but we can build a system which can cover many

0:44:29.548 --> 0:44:33.655
tags, many languages and then also need less
data for each other.

0:44:39.639 --> 0:44:51.067
With invasion maybe some presentation of everything
is a bit cat that can say the most important.

0:44:51.331 --> 0:45:09.053
So machine translation started coming from
information theory in there was this: It's

0:45:09.053 --> 0:45:13.286
treating machine translation as encryption
or decryption.

0:45:13.533 --> 0:45:21.088
Don't understand it, want to have it in English,
treat it as if it's like encrypted English,

0:45:21.088 --> 0:45:28.724
and then apply my decryption algorithm, which
they were working a lot during the Second World

0:45:28.724 --> 0:45:29.130
War.

0:45:29.209 --> 0:45:34.194
And so if I cannot do this detruction then
this sings a song.

0:45:34.934 --> 0:45:42.430
And they based on that they had rules and
so on.

0:45:42.430 --> 0:45:50.843
So they had the judge Georgetown experiments
in where.

0:45:51.691 --> 0:45:57.419
From English and then they were like wow.

0:45:57.419 --> 0:46:01.511
This is solved in some years.

0:46:01.511 --> 0:46:04.921
Now we can do sentences.

0:46:06.546 --> 0:46:18.657
As you can imagine this didn't really work
out that way, so it's not really happening.

0:46:18.657 --> 0:46:24.503
The spirit is willing, but flesh is weak.

0:46:24.444 --> 0:46:30.779
Translated it to Russian and then to Germany
and then vodka is good but the meat is rotten.

0:46:31.271 --> 0:46:39.694
Think it never really happened this way, but
you can see you can imagine that something

0:46:39.694 --> 0:46:49.533
like that could happen, and then in in the
there was this report saying: It's more challenging

0:46:49.533 --> 0:46:56.877
than expected and the problem is that we have
to invest more.

0:46:56.877 --> 0:47:02.801
There's no benefit for doing machine translation.

0:47:04.044 --> 0:47:09.255
At least in some other countries there was
a bit, but then for some time there wasn't

0:47:09.255 --> 0:47:10.831
that big out of progress.

0:47:12.152 --> 0:47:26.554
We have then in the' 70s there were some rule
based systems that would cover out some linguistic

0:47:26.554 --> 0:47:28.336
background.

0:47:28.728 --> 0:47:34.013
They are now doing very good machine translation,
but they had a really huge rule base.

0:47:34.314 --> 0:47:43.538
So they really have like handwritten roots
how to parse sentences, how to translate parse

0:47:43.538 --> 0:47:45.587
sentences to parse.

0:47:46.306 --> 0:47:55.868
When which word should be translated, these
rule based systems were quite strong for a

0:47:55.868 --> 0:47:57.627
very long time.

0:47:57.917 --> 0:48:03.947
So even in or so for some language fares and
some remains, it was better than a machine

0:48:03.947 --> 0:48:04.633
learning.

0:48:05.505 --> 0:48:09.576
Well, of course, there was a lot of effort
in and a lot of experts were building this.

0:48:11.791 --> 0:48:13.170
And then.

0:48:13.053 --> 0:48:18.782
The first statistical machine translations
were coming in the early nineties.

0:48:18.782 --> 0:48:25.761
There's the system by IBM will refer to them
as a T by the IBM models, which are quite famous,

0:48:25.761 --> 0:48:32.886
and they were used to film your machine translations
from the nineties nineties to two thousand.

0:48:32.912 --> 0:48:35.891
Fifteen or so people were working on the IBM
models.

0:48:36.496 --> 0:48:44.608
And that was the first way of doing a machine
translation with statisticals or machine learning.

0:48:44.924 --> 0:48:52.143
And it was possible through the French English
under a corpusol from the Canadian Parliament

0:48:52.143 --> 0:48:59.516
they also had proceedings in French and English
and people tried to use that to translate and.

0:49:01.681 --> 0:49:06.919
And yes, so that was than the start of statistical
machine translation.

0:49:07.227 --> 0:49:17.797
Is called a phrase page machine translation
was introduced where you could add more information

0:49:17.797 --> 0:49:26.055
in use longer chunks to translate and phrase
page translation was somehow.

0:49:26.326 --> 0:49:27.603
She'll Start Fourteen.

0:49:27.767 --> 0:49:37.721
With this straight space machine sensation
we saw the first commercial systems.

0:49:38.178 --> 0:49:45.301
And yeah, that was the first big advantage
where really you can see the machine translation.

0:49:47.287 --> 0:49:55.511
And neural machine translation was mainly
introduced.

0:49:55.511 --> 0:50:07.239
That means there was a shift from traditional
statistical modeling to using.

0:50:07.507 --> 0:50:09.496
And that was quite impressive.

0:50:09.496 --> 0:50:11.999
It was really within one or two years.

0:50:11.999 --> 0:50:17.453
The whole research community shifted from
what they had been working on since twenty

0:50:17.453 --> 0:50:17.902
years.

0:50:17.902 --> 0:50:23.485
And everybody was using this pattern, you
know networks, because just the performances

0:50:23.485 --> 0:50:25.089
were really really much.

0:50:25.425 --> 0:50:35.048
Especially they are what we also see now with
chat boards like the impressive thing.

0:50:35.135 --> 0:50:45.261
That was very, very challenging if you see
machine translation before that, especially

0:50:45.261 --> 0:50:47.123
if the English.

0:50:47.547 --> 0:50:53.352
But if you were transmitting to German you
would see that the agreement so that it's there

0:50:53.352 --> 0:50:58.966
shown abound and dishewn and boima and this
didn't always really work perfect maybe for

0:50:58.966 --> 0:51:04.835
the short range of work but then it has to
be accusative and it's like far away then things

0:51:04.835 --> 0:51:06.430
didn't really work well.

0:51:06.866 --> 0:51:13.323
Now with new machine translation we have a
bit of a different problem: So the sentences

0:51:13.323 --> 0:51:16.901
are typically really nice.

0:51:16.901 --> 0:51:24.056
They are perfectly written not always but
very often.

0:51:24.224 --> 0:51:36.587
So that adequacy and their conveillance should
have the same meaning is typically the bigger.

0:51:42.002 --> 0:51:46.039
So how can we do so last?

0:51:46.039 --> 0:51:54.889
What are the things and how can we do machine
rendering?

0:51:55.235 --> 0:52:01.297
So we had first blue based systems, and as
a side systems we did that we manually created

0:52:01.297 --> 0:52:01.769
rules.

0:52:01.861 --> 0:52:07.421
And there were rules how to dissemvy real
ambiguities.

0:52:07.421 --> 0:52:16.417
For example, we had the word banks look at
the context and do rules like to decide when.

0:52:17.197 --> 0:52:28.418
How to translate the structure, but you know
how to transfer the structure that you work

0:52:28.418 --> 0:52:33.839
has to split it in German and move to the.

0:52:35.295 --> 0:52:36.675
Here's a difficult thing.

0:52:36.675 --> 0:52:39.118
My thing is you don't need any training data.

0:52:39.118 --> 0:52:41.295
It's not like now with machine learning.

0:52:41.295 --> 0:52:46.073
If you build a machine translation system,
the first question you should ask is do I have

0:52:46.073 --> 0:52:46.976
data to do that?

0:52:46.976 --> 0:52:48.781
Do I have parallel data to train?

0:52:49.169 --> 0:52:50.885
Here there's no data.

0:52:50.885 --> 0:52:57.829
It's like all trades, pencils and roads, but
the problem is people trading the roads and

0:52:57.829 --> 0:52:59.857
this needs to be experts.

0:52:59.799 --> 0:53:06.614
Understand at least the grammar in one language,
basically the grammar in both languages.

0:53:06.614 --> 0:53:09.264
It needs to be a real language to.

0:53:10.090 --> 0:53:17.308
Then we have the two corpus based machine
translation approaches, and then we use machine

0:53:17.308 --> 0:53:22.682
learning to learn how to translate from one
language to the other.

0:53:22.882 --> 0:53:29.205
We should find out ourselves what is the meaning
of individual words, which words translate

0:53:29.205 --> 0:53:30.236
to each other.

0:53:30.236 --> 0:53:36.215
The only information we give is the German
sentence, the English sentence, and then we

0:53:36.215 --> 0:53:37.245
look for many.

0:53:37.697 --> 0:53:42.373
So maybe you think there's a Bible for each
language.

0:53:42.373 --> 0:53:44.971
There shouldn't be a problem.

0:53:45.605 --> 0:53:52.752
But this is not the scale when we're talking
about.

0:53:52.752 --> 0:54:05.122
Small systems have maybe one hundred thousand
sentences when we're building large models.

0:54:05.745 --> 0:54:19.909
The statistical models do statistics about
how the word screw occur and how often the

0:54:19.909 --> 0:54:21.886
word screw.

0:54:22.382 --> 0:54:29.523
While we were focused on it was currently
most of the cases referred to as neural communication.

0:54:30.050 --> 0:54:44.792
So in this case the idea is that you have
a neural model which is a big neural network.

0:54:45.345 --> 0:54:55.964
And for these machine drums there quite challenging
tasks.

0:54:55.964 --> 0:55:03.883
For example, this transformal architecture.

0:55:03.903 --> 0:55:07.399
Cast by Google in two thousand eight.

0:55:08.028 --> 0:55:19.287
Here want to ask the screw-based machine translation
of that part.

0:55:22.862 --> 0:55:33.201
Would say it's mainly rule based systems because
purely rule based systems maybe exist with

0:55:33.201 --> 0:55:36.348
some very exotic languages.

0:55:36.776 --> 0:55:43.947
Of course, the idea of investigating if we
have this type of rulers that might be still

0:55:43.947 --> 0:55:45.006
interesting.

0:55:45.105 --> 0:55:52.090
Maybe you can try to let someone force the
rules in there.

0:55:52.090 --> 0:55:57.655
You might use rules to create artificial data.

0:55:57.557 --> 0:56:03.577
That it might be helpful to have some concepts
which develop by bilinguistic researches to

0:56:03.577 --> 0:56:09.464
somehow interview that that's still an open
question is sometimes helpful, and of course

0:56:09.464 --> 0:56:13.235
is also interesting from more the analyzed
perspectives.

0:56:13.235 --> 0:56:13.499
So.

0:56:13.793 --> 0:56:20.755
Do the new networks have these types of concepts
of gender or anything?

0:56:20.755 --> 0:56:23.560
And can we test that though?

0:56:30.330 --> 0:56:34.255
Yes, and then the other way of describing
how this can be done.

0:56:34.574 --> 0:56:52.021
And then originally mainly for a rule based
system that can be used for a lot of scenarios.

0:56:52.352 --> 0:57:04.135
In real ways, the first world has really direct
translation systems that work for related languages.

0:57:04.135 --> 0:57:11.367
You mainly look at each word and replace the
word by the one.

0:57:11.631 --> 0:57:22.642
Another idea is that you first do some type
of animus on the source side, so for example

0:57:22.642 --> 0:57:28.952
you can create what is referred to as a path
tree.

0:57:30.150 --> 0:57:36.290
Or you can instead, and that is what is called
the lingua face approach.

0:57:36.290 --> 0:57:44.027
You take the short sentence and parse it into
a semantic representation, which is hopefully

0:57:44.027 --> 0:57:44.448
the.

0:57:44.384 --> 0:57:50.100
Only of the meaning of what is said and then
you can generate it to any other language because

0:57:50.100 --> 0:57:55.335
it has a meaning and then you can need a part
generation which can generate all other.

0:57:57.077 --> 0:58:09.248
The idea is somewhat nice to have this type
of interlingua, general representation of all

0:58:09.248 --> 0:58:17.092
meanings, and they always translate into the
interlingua.

0:58:17.177 --> 0:58:19.189
A Little World and It's Been Somewhere.

0:58:20.580 --> 0:58:26.684
It shouldn't be a natural language because
it shouldn't have ambiguities so that's a big

0:58:26.684 --> 0:58:32.995
difference so the story and the tiger language
have ambiguities so the idea is they do some

0:58:32.995 --> 0:58:39.648
semantic representation or what does it mean
and so on and therefore it's very easy to generate.

0:58:41.962 --> 0:58:45.176
However, that is a challenge that this really
exists.

0:58:45.176 --> 0:58:48.628
You cannot define the language for anything
in the world.

0:58:49.249 --> 0:58:56.867
And that's why the Lingo-based approach typically
worked for small domains to do hotel reservation,

0:58:56.867 --> 0:59:00.676
but if you want to define the Lingo for anything.

0:59:01.061 --> 0:59:07.961
There have been approaches and semantics,
but it's yeah, it's not really possible CR.

0:59:07.961 --> 0:59:15.905
So approaches to this because I mean a seasonal
vector's face and bitch eyes and slaves everything

0:59:15.905 --> 0:59:20.961
that I mitonized that they all could end up
in the same space.

0:59:21.821 --> 0:59:24.936
That is not the question.

0:59:24.936 --> 0:59:35.957
If you talk about neural networks, it's direct
translation on the one you're putting in the

0:59:35.957 --> 0:59:36.796
input.

0:59:36.957 --> 0:59:44.061
And you can argue for both that we have been
making this representation language agnostic

0:59:44.061 --> 0:59:45.324
or independent.

0:59:47.227 --> 0:59:52.912
Until now we were able to make it less language
dependent but it's very hard to make it completely

0:59:52.912 --> 0:59:54.175
language independent.

0:59:54.175 --> 0:59:59.286
Maybe it's also not necessary and of course
if there's again the problem there's not all

0:59:59.286 --> 1:00:04.798
information and the source and the target there
is different types of information if you remove

1:00:04.798 --> 1:00:05.602
all language.

1:00:05.585 --> 1:00:09.408
Information might be that you have removed
too many information.

1:00:10.290 --> 1:00:15.280
Talk about this and there's a very interesting
research direction in which we are working

1:00:15.280 --> 1:00:20.325
on on the multilingual part because there is
especially the case if we have several source

1:00:20.325 --> 1:00:25.205
languages, several type of languages who try
to generate a representation in the middle

1:00:25.205 --> 1:00:27.422
which have the few language dependence.

1:00:32.752 --> 1:00:46.173
Yes, so for a direct base approach, so as
said the first one is dictionary based approach.

1:00:46.806 --> 1:00:48.805
Replace some words with other words.

1:00:48.805 --> 1:00:51.345
Then you have exactly the same same structure.

1:00:51.771 --> 1:00:55.334
Other problems are one to one correspondence.

1:00:55.334 --> 1:01:01.686
Some phrases are expressed with several words
in English, but one word in German.

1:01:01.686 --> 1:01:03.777
That's extremely the case.

1:01:03.777 --> 1:01:07.805
Just think about all our composites like the
Donau.

1:01:08.608 --> 1:01:18.787
Which is used very often as been referred
to as translation memory.

1:01:18.787 --> 1:01:25.074
It might seem very simple, but it's like.

1:01:26.406 --> 1:01:33.570
That means you might think of this not helpful
at all, but you know think about translating.

1:01:33.513 --> 1:01:38.701
The law text is more like the interactive
scenario for the human translator.

1:01:38.701 --> 1:01:44.091
In law text there is a lot of repetition and
a lot of phrases occur very often.

1:01:44.424 --> 1:01:55.412
The translator has just a background of translation
memory and retrieve all this translation.

1:01:55.895 --> 1:02:07.147
There is even another benefit in addition
to less work: That is also precise in the way

1:02:07.147 --> 1:02:19.842
know this creates a small mistake in the North
Carolina.

1:02:20.300 --> 1:02:22.584
By especially its like consistence,.

1:02:23.243 --> 1:02:32.954
If you once translate the sentence this way
you again translate it and especially for some

1:02:32.954 --> 1:02:36.903
situations like a company they have.

1:02:37.217 --> 1:02:47.695
With this one, of course, you get more consistent
translations.

1:02:47.695 --> 1:02:56.700
Each one is a style where phrases maybe are
retrieved.

1:03:01.861 --> 1:03:15.502
Then we have these transfer based approaches
where we have three steps: Analysts remain

1:03:15.502 --> 1:03:25.975
that you check one synthetic structure, so
for example for morphology the basic.

1:03:26.286 --> 1:03:37.277
Then you will do a parstry or dependency structure
that this is the adjective of the balm.

1:03:37.917 --> 1:03:42.117
Then you can do the transfer where you transfer
the structure to the other.

1:03:42.382 --> 1:03:46.633
There you have to do, for example, it's re-ordering
because the satisfaction is different.

1:03:46.987 --> 1:03:50.088
In German, the adjective is before the noun.

1:03:50.088 --> 1:03:52.777
In Spanish, it's the other way around.

1:03:52.777 --> 1:03:59.256
You have first found and then that it's nice
and these types of rehonoring can be done there.

1:03:59.256 --> 1:04:04.633
You might have to do other things like passive
voice to exit voice and so on.

1:04:05.145 --> 1:04:14.074
And in some type of lexical transverse it
should like to me: And then you are doing the

1:04:14.074 --> 1:04:16.014
generation.

1:04:16.014 --> 1:04:25.551
Of course, you would do the agreement if it
is accusative.

1:04:25.551 --> 1:04:29.430
What type of adjective?

1:04:30.090 --> 1:04:32.048
Is some kind of saving.

1:04:32.048 --> 1:04:39.720
Of course, here, because the analyze has only
to be done in the source language, the transfer

1:04:39.720 --> 1:04:41.679
has to do on the pairs.

1:04:41.679 --> 1:04:48.289
But if you not look German, English and French
through all directions, you only.

1:04:53.273 --> 1:04:59.340
Then there is an interlingua card which is
really about the pure meaning, so you have

1:04:59.340 --> 1:05:00.751
a semantic grammar.

1:05:01.061 --> 1:05:07.930
To represent everything and one thing, one
nice implication is more extreme than before.

1:05:07.930 --> 1:05:15.032
You don't have the transfer anymore, so if
you add one language to it and you have already.

1:05:15.515 --> 1:05:26.188
If you add the one parting and the one generation
phase, you can now translate from: So you need

1:05:26.188 --> 1:05:40.172
components which do the and components which
do the generation, and then you can translate:

1:05:41.001 --> 1:05:45.994
You can also do other things like paraphrasing.

1:05:45.994 --> 1:05:52.236
You can translate back to the words language
and hopefully.

1:05:53.533 --> 1:06:05.013
If you're sparkling trying to analyze it,
it was also down a lot for ungrammetical speech

1:06:05.013 --> 1:06:11.518
because the idea is you're in this representation.

1:06:12.552 --> 1:06:18.679
Of course, it's very much work and it's only
realistic for limited domains.

1:06:20.000 --> 1:06:25.454
Then we're, we're have the campus based approach.

1:06:25.745 --> 1:06:32.486
So we'll talk about a lot about peril layer
and what is really peril data is what you know

1:06:32.486 --> 1:06:34.634
from the Rosetta stone page.

1:06:34.634 --> 1:06:41.227
That is, you have a sewer sentence and you
have a target sentence and you know they need

1:06:41.227 --> 1:06:42.856
to watch translation.

1:06:43.343 --> 1:06:46.651
And that's important, so the alignment is
typically at a sentence level.

1:06:46.987 --> 1:06:50.252
So you know, for each sentence what is a translation?

1:06:50.252 --> 1:06:55.756
Not always perfect because maybe there's two
German sentences and one English, but at that

1:06:55.756 --> 1:06:57.570
level it's normally possible.

1:06:57.570 --> 1:07:03.194
At word level you can't do that because it's
a very complicated thing and sense level that's

1:07:03.194 --> 1:07:04.464
normally a relative.

1:07:05.986 --> 1:07:12.693
Some type of machine learning which tries
to learn dismapping between sentences on the

1:07:12.693 --> 1:07:14.851
English side and sentences.

1:07:15.355 --> 1:07:22.088
Of course this doesn't look like good mapping
too complex but you try to find something like

1:07:22.088 --> 1:07:28.894
that where it's a very nice mapping so there's
always the mixing things are met to each other

1:07:28.894 --> 1:07:32.224
and then if you have the English you can try.

1:07:32.172 --> 1:07:36.900
In another English sentence you can apply
the same mannering and hopefully adhere to

1:07:36.900 --> 1:07:38.514
the right sentence in terms.

1:07:38.918 --> 1:07:41.438
The big problem here.

1:07:41.438 --> 1:07:44.646
How can we find this model?

1:07:44.646 --> 1:07:50.144
How to map English centers into German centers?

1:07:54.374 --> 1:08:08.492
How we do that is that we are trying to maximize
the probability, so we have all the letterstone.

1:08:09.109 --> 1:08:15.230
Then we're having some type of model here
which takes the Suez language and translates

1:08:15.230 --> 1:08:16.426
it for a target.

1:08:16.896 --> 1:08:34.008
And then we are in our translation, and we
are adjusting our model in a way that the probability.

1:08:34.554 --> 1:08:48.619
How that is the idea behind it, how we are
pushed now, implement that is part of the bottle.

1:08:51.131 --> 1:09:01.809
And then if we want to do translation, what
we are doing is we are trying to find the translation.

1:09:01.962 --> 1:09:06.297
So we are scoring many possible translations.

1:09:06.297 --> 1:09:12.046
There is an infinite number of sentences that
we are trying.

1:09:12.552 --> 1:09:18.191
That may be a bit of a problem when we talk
about confidence because we are always trying

1:09:18.191 --> 1:09:19.882
to find the most probable.

1:09:20.440 --> 1:09:28.241
And then, of course, we are not really having
intrinsically the possibility to say, oh, I

1:09:28.241 --> 1:09:31.015
have no idea in this situation.

1:09:31.015 --> 1:09:35.782
But our general model is always about how
can we find?

1:09:40.440 --> 1:09:41.816
Think It's.

1:09:42.963 --> 1:09:44.242
Get Four More Slides.

1:09:46.686 --> 1:09:52.025
So just high level, so for a proper space
this one we won't cover again.

1:09:52.352 --> 1:10:00.808
Its example based machine translation was
at the beginning of SMT.

1:10:00.808 --> 1:10:08.254
The idea is that you take subparts and combine
them again.

1:10:08.568 --> 1:10:11.569
So this will not be really covered here.

1:10:11.569 --> 1:10:15.228
Then the statistical machine translation we
will.

1:10:17.077 --> 1:10:18.773
Yeah, we will cover next week.

1:10:19.079 --> 1:10:27.594
The idea is there that we automatically now,
if we have the sentence alignment, we automatically.

1:10:27.527 --> 1:10:34.207
In the sentences, and then we can learn statistical
models of how probable words are translated

1:10:34.207 --> 1:10:39.356
to each other, and then the surge is that we
create different hypotheses.

1:10:39.356 --> 1:10:45.200
This could be a translation of this part,
this could be a translation of that part.

1:10:45.200 --> 1:10:47.496
We give a score to each of them.

1:10:47.727 --> 1:10:51.584
The statistical machine manual is where a
lot of work is done.

1:10:51.584 --> 1:10:54.155
How can we score how good translation is?

1:10:54.494 --> 1:11:04.764
The words can recur this type of structure,
how is it reordered, and then based on that

1:11:04.764 --> 1:11:08.965
we search for the best translation.

1:11:12.252 --> 1:11:19.127
Then yeah, that one what we'll cover most
of the time is is a neural, a model where we

1:11:19.127 --> 1:11:21.102
can use neural networks.

1:11:21.102 --> 1:11:27.187
The nice thing is between everything together
before we get some compliment.

1:11:27.187 --> 1:11:30.269
Each of them is trained independently.

1:11:30.210 --> 1:11:34.349
Which of course has a disadvantage that they
might not best work together.

1:11:34.694 --> 1:11:36.601
Here everything is trained together.

1:11:36.601 --> 1:11:39.230
The continuous representation will look into
that.

1:11:39.339 --> 1:11:41.846
That's very helpful soft.

1:11:41.846 --> 1:11:50.426
We then neonetworks are able to learn somehow
the relation between words and that's very

1:11:50.426 --> 1:11:57.753
helpful because then we can more easily deal
with words which didn't occur.

1:12:00.000 --> 1:12:05.240
One thing just to correlate that to interlingua
based.

1:12:05.345 --> 1:12:07.646
So we have this as an actual language.

1:12:07.627 --> 1:12:11.705
And if you do an interlingual based approach
but don't take an artificial.

1:12:11.731 --> 1:12:17.814
With no ambiguities, but with a natural language
that's referred to as pivot based in tea and

1:12:17.814 --> 1:12:20.208
can be done with all the approaches.

1:12:20.208 --> 1:12:25.902
So the ideas instead of directly translating
from German to French, you first translate

1:12:25.902 --> 1:12:29.073
from German to English and then from English
to.

1:12:29.409 --> 1:12:40.954
French where the big advantage is that you
might have a lot more data for these two directions

1:12:40.954 --> 1:12:43.384
than you have here.

1:12:44.864 --> 1:12:54.666
With this thank you and deserve more questions
and a bit late I'm sorry and then I'll see

1:12:54.666 --> 1:12:55.864
you again.