| { | |
| "paper_id": "2021", | |
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| "date_generated": "2023-01-19T13:27:42.530443Z" | |
| }, | |
| "title": "discopy: A Neural System for Shallow Discourse Parsing", | |
| "authors": [ | |
| { | |
| "first": "Ren\u00e9", | |
| "middle": [], | |
| "last": "Knaebel", | |
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| "institution": "University of Potsdam", | |
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| "email": "[email protected]" | |
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| "abstract": "This paper demonstrates discopy, a novel framework that makes it easy to design components for end-to-end shallow discourse parsing. For the purpose of demonstration, we implement recent neural approaches and integrate contextualized word embeddings to predict explicit and non-explicit discourse relations. Our proposed neural feature-free system performs competitively to systems presented at the latest Shared Task on Shallow Discourse Parsing. Finally, a web front end is shown that simplifies the inspection of annotated documents. The source code, documentation, and pretrained models are publicly accessible.", | |
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| "text": "This paper demonstrates discopy, a novel framework that makes it easy to design components for end-to-end shallow discourse parsing. For the purpose of demonstration, we implement recent neural approaches and integrate contextualized word embeddings to predict explicit and non-explicit discourse relations. Our proposed neural feature-free system performs competitively to systems presented at the latest Shared Task on Shallow Discourse Parsing. Finally, a web front end is shown that simplifies the inspection of annotated documents. The source code, documentation, and pretrained models are publicly accessible.", | |
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| "text": "Whenever people compose text, they (consciously or not) make sure that related sentences are cohesive and coherent within a common section. By using models to analyze discourse, we identify relations within a text that consist of phrases and a certain sense. These discourse relations and their understanding are important for other tasks in the NLP community such as abstractive summarization (Gerani et al., 2014 ), text simplification (Zhong et al., 2020) , and argumentation mining (Hewett et al., 2019) . The Penn Discourse Treebank (PDTB) (Prasad et al., 2008) , for example, describes discourse as a set of individual relations. The following two (artificial) examples demonstrate the main types of relations annotated in their corpus:", | |
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| "end": 414, | |
| "text": "(Gerani et al., 2014", | |
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| "text": "(Zhong et al., 2020)", | |
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| "text": "(Hewett et al., 2019)", | |
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| "text": "(Prasad et al., 2008)", | |
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| "section": "Introduction", | |
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| "text": "1. While it is raining outside, I clean the dishes. Relations consist of two arguments (Arg1 in italics and Arg2 in bold) and a sense (e.g. Tempo-ral.Synchronous 1 ) is assigned. The first example is called an explicit relation, because the relation is signaled by a phrase called connective (Conn is underlined). The second example refers to the group of implicit relations because of the absence of a connective that signals the relation. Additionally, if adjacent sentences have an entity-based relation, they are marked as EntRel.", | |
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| "section": "Introduction", | |
| "sec_num": "1" | |
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| "text": "The area of shallow discourse parsing (SDP) aims to build models finding aforementioned discourse structures. Started through the development of PDTB, shallow discourse parsing gained more awareness by two shared Tasks (Xue et al., 2015 (Xue et al., , 2016 . Successful systems at the last competition were those of Wang and Lan (2016) ; Qin et al. (2016) ; Schenk et al. (2016) ; Oepen et al. (2016) ; .", | |
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| "text": "This work introduces an end-to-end neural system that implements recently researched components for these tasks. Our goal is to design components that all rely on the same contextualized word embeddings as input and, thus, it avoids the necessity to train huge neural networks. We compare our results with state-of-the-art shallow discourse parsers that took part at the CoNLL Shared Task in 2016 (Xue et al., 2016) which is to our knowledge the last time full systems were published. The contributions of this paper are as follows:", | |
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| "text": "1. We introduce a simple modular and easily extendable framework for shallow discourse parsing. We also provide pretrained systems by recent advances in word representations that demonstrate competitive performance compared to existing systems.", | |
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| "text": "2. We provide a first version of a web front end to visualize parser outputs and, optionally, to connect a parser via REST API.", | |
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| "text": "In our framework discopy, we design a clean and modular parser pipeline principle. Each component has to implement a specified interface that assumes to process a well defined document structure. Starting with a document without annotated discourse relations, each component further adds its predictions while the document is passed through the pipeline, for example one component identifies connectives and their sense and, based on this prediction, another component extracts arguments around these connectives to complete the explicit relations. This parser architecture makes the definition of a custom pipeline easy and components remain interchangeable. Together with the framework, we provide multiple components for an endto-end neural shallow discourse parser. The simple pipeline interface (exemplified in Listing 2) with methods such as fit, parse, and eval, ensures pleasant user experience. The system combines recent research on various subtasks with advances in contextualized word embeddings. We make the framework and pre-trained components available under https://github.com/rknaebel/discopy. In the following, we briefly summarize the components implemented in our system. Contextualized Word Embeddings. We start by generating contextualized embeddings for each document. These embeddings come from a chosen BERT architecture that is provided by the Hugging Face transformers library (Wolf et al., 2020) . Pre-tokenized sentences are processed again by a specific BERT tokenizer which results in a possibly higher number of sub-tokens as the input sequence. For each input token, we select and concatenate the last four hidden layers that correspond to the first sub-token, following the principle demonstrated by Devlin et al. (2019) that performed best using the BERT architecture as a feature extractor. Each component in our discourse parser uses the same input embeddings. This has the advantage, to only produce the computation-intense embeddings once in the beginning. Connective Sense Classifier. The first component in the pipeline refers to the problem of connective disambiguation and explicit sense classification. This component is based on the work of Knaebel and Stede (2020) of which we use the jointly trained model to keep the number of components as small as possible. The component first extracts connective candidates based on a pattern-matching approach. Then, these candidates are classified as having a sentential meaning or a connective meaning which is indicated by a predicted sense class. The idea of using the same features for both tasks originates from Pitler and Nenkova (2009) . Explicit Connective Argument Extractor. The component that is responsible to extract explicit arguments creates a window surrounding the previously predicted connective and searches for corresponding arguments (Arg1 and Arg2) within this span. We extend the connective argument extractor of Knaebel et al. (2019) and replace GloVe embeddings (Pennington et al., 2014) by our contextualized embeddings. Implicit Sense Classifier. Implicit relations are annotated on adjacent sentences within the same paragraph that do not contain any other explicit relation. For the implicit sense classifier, we choose a simple approach and collect all implicit candidate sentence pairs. We follow previous work by Rutherford et al. (2017) which has been recently chosen as a baseline in work by Liang et al. (2020) . There, embeddings for both arguments are collected and processed by a recurrent neural network. Then, both intermediate representations are combined using a maximum pooling layer and given to a fully connected layer to predict the implicit sense. We adopt this approach by using our contextualized embeddings, and we add a labels for entity-based relations (EntRel) as well as a for the absence of any relation (NoRel) to the final set of predictions.", | |
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| "section": "System Architecture", | |
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| "text": "In conjunction with our system, we provide a prototypical web-based front end with several views that simplifies the inspection of parser results. It can be used to visualize annotated documents, parse docu- ments, and inspect them interactively. Further, the visual inspection makes it easier to identify weaknesses of a developed parser, e.g. inaccurate connective identification or misplaced argument spans. The system is built on top of a simple Python web service powered by FastAPI 2 and it is designed using a reactive JavaScript library called VueJS 3 . Both sides communicate via RESTful interface with each other. Figure 1 demonstrates the view of a selected document. At the top, there is a simple search bar for easily accessing documents. Directly below, the document's text is shown together with a list of its discourse relations at the bottom of the view. Then, each discourse relation is listed with its corresponding relation type (e.g. Explicit, Implicit, EntRel, AltLex), as well as its sense. Different parts of a relation, first and second arguments as well as the connective, are highlighted by colors. Additionally, some context tokens are given before and after the relation if possible.", | |
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| "text": "The connective sense classifier takes the connective candidate and one surrounding context tokens as input. The two hidden layers both have dimensions 256. A dropout layer follows after each hidden layer with a 0.3 drop rate. Both argument extractors use two bi-directional recurrent layers with a hidden size of 256 in each direction, a dropout rate of 0.2 before and after the fully connected layer having a dimension of 128. The explicit extractor is trained on explicit examples with a window size of 100. The implicit sense classifier accepts two arguments with at most 35 tokens each. The same bi-directional recurrent layer with hidden size 128 processes both arguments. The drop rate of the dropout layer that follows after concatenation is 0.25, and the size of the hidden dimension afterward is 128. All models are trained with AMSGrad Adam (Reddi et al., 2018) and a 0.001 learning rate.", | |
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| "text": "For our evaluation, we use the same experimental design as proposed at the CoNLL Shared Task 2016 (Xue et al., 2016) . We have trained multiple pipelines with varying language models used to compute the input embeddings. The model names in our experiment tables follow the naming of the word embedding model 4 . As comparison systems, we choose the overall best performing submissions", | |
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| "text": "Explicits Implicits for each test set (ecnucs (Wang and Lan, 2016) and oslopots (Oepen et al., 2016) ). Similar to the Shared Task, we compare two different thresholds: strict refers to an exact match of relations while partial counts relations as correct if their overlap is at least 70 %. Performances are measured on Section 23 of the PDTB as well as PDTB-like annotated Wikipedia data prepared for the Shared Task.", | |
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| "text": "In Table 1 , strict results of the studied models are summarized. In the table's left part pointing to explicit relations, neural models outperform traditional models in most categories except for Arg1 identification, but still results are pretty close. Especially the bert-base model has an overall high performance. Interestingly, albert-base performs quite good, even though it has the smallest number of trained parameters. Also, it seems not beneficial in our experimental design to use a large version of a model. A reason for that might be the higher number of input values per token leading the model to an unstable training. The performance for implicit relations (arguments and overall) is noticeably weak as we used I fairly simple method for recognizing arguments following Lin et al. (2014) . Thereby, the overall implicit results are lowered as relations were not counted as correct during evaluation. The partial scores in Table 2 were expected to be higher compared to their strict counter parts.", | |
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| "text": "In this work, we demonstrated a framework for shallow discourse parsing and integrated a web-based graphical user interface to study parser outputs. We presented an exemplary pipeline built from neural components that are based on recent models. We extend previous approaches to incorporate precomputed contextualized word embeddings. Our pipeline system performs competitively to former discourse parsers and partially outperforms them, while not using any linguistic features.", | |
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| "text": "In the future, we plan to integrate more recent neural architectures into the system to improve overall scores. Further, we want to improve the graphical interface to seamlessly integrate the visualization directly into the text such as done with brat 5 for dependency parses. Thus, it will be easier to compare multiple relations in their context. Overlapping arguments are a major problem, why we decided to list relations separately. Building on that a view would be useful for comparing discourse relations across documents, e.g. comparing predictions and gold annotations or multiple parser outputs.", | |
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| "text": "Explicits Implicits ", | |
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| "text": "Senses are ordered in a hierarchy of up to three levels. Temporal refers to the first level and Synchronous to the second, respectively.", | |
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| "text": "This research was funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation), project 455911521.", | |
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| "FIGREF0": { | |
| "num": null, | |
| "uris": null, | |
| "text": "Temporal.Synchronous) 2. Yesterday I walked through the rain, today I am sick. (Contingency.Cause)", | |
| "type_str": "figure" | |
| }, | |
| "FIGREF1": { | |
| "num": null, | |
| "uris": null, | |
| "text": "load pdtb documents with contextualized embeddings docs_train = load_bert_conll_dataset(...) docs_val = load_bert_conll_dataset(...) # definition of the parser pipeline by list of components parser = ParserPipeline([ # add connective and its sense ConnectiveSenseClassifier(...), # use connective to add explicit arguments ConnectiveArgumentExtractor(...), # extract adjacent sentences without explicit relation ImplicitArgumentExtractor(), # attach implicit sense ArgumentSenseClassifier(...), ]) # fit custom pipeline on annotated documents parser.fit(docs_train, docs_val) # extract discourse relations parses = parser.parse(docs_val)Listing 1: Discopy pipeline source code example.", | |
| "type_str": "figure" | |
| }, | |
| "FIGREF2": { | |
| "num": null, | |
| "uris": null, | |
| "text": "Web-based front end overview. A selected document is shown with its discourse relations in the bottom.", | |
| "type_str": "figure" | |
| }, | |
| "TABREF1": { | |
| "num": null, | |
| "text": "Experimental results (strict F1 scores) for Section 23 of WSJ and the blind data set proposed for CoNLL Shared Task.", | |
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| "content": "<table/>", | |
| "html": null | |
| }, | |
| "TABREF3": { | |
| "num": null, | |
| "text": "Experimental results (partial F1 scores with 0.7 overlap) for Section 23 of WSJ and the blind data set proposed for CoNLL Shared Task.", | |
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| "content": "<table/>", | |
| "html": null | |
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| } | |
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