| { | |
| "paper_id": "2020", | |
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| "generated_with": "S2ORC 1.0.0", | |
| "date_generated": "2023-01-19T11:59:39.569036Z" | |
| }, | |
| "title": "Zero-Shot Neural Machine Translation: Russian-Hindi @LoResMT 2020", | |
| "authors": [ | |
| { | |
| "first": "Rahman", | |
| "middle": [], | |
| "last": "Sahinur", | |
| "suffix": "", | |
| "affiliation": { | |
| "laboratory": "", | |
| "institution": "National Institute of Technology Silchar Assam", | |
| "location": { | |
| "country": "India" | |
| } | |
| }, | |
| "email": "[email protected]" | |
| }, | |
| { | |
| "first": "Abdullah", | |
| "middle": [], | |
| "last": "Laskar", | |
| "suffix": "", | |
| "affiliation": { | |
| "laboratory": "", | |
| "institution": "National Institute of Technology Silchar Assam", | |
| "location": { | |
| "country": "India" | |
| } | |
| }, | |
| "email": "[email protected]" | |
| }, | |
| { | |
| "first": "Rahman", | |
| "middle": [], | |
| "last": "Faiz Ur", | |
| "suffix": "", | |
| "affiliation": { | |
| "laboratory": "", | |
| "institution": "National Institute of Technology Silchar Assam", | |
| "location": { | |
| "country": "India" | |
| } | |
| }, | |
| "email": "" | |
| }, | |
| { | |
| "first": "Partha", | |
| "middle": [], | |
| "last": "Khilji", | |
| "suffix": "", | |
| "affiliation": { | |
| "laboratory": "", | |
| "institution": "National Institute of Technology Silchar Assam", | |
| "location": { | |
| "country": "India" | |
| } | |
| }, | |
| "email": "[email protected]" | |
| }, | |
| { | |
| "first": "Sivaji", | |
| "middle": [], | |
| "last": "Pakray", | |
| "suffix": "", | |
| "affiliation": { | |
| "laboratory": "", | |
| "institution": "National Institute of Technology Silchar Assam", | |
| "location": { | |
| "country": "India" | |
| } | |
| }, | |
| "email": "" | |
| }, | |
| { | |
| "first": "", | |
| "middle": [], | |
| "last": "Bandyopadhyay", | |
| "suffix": "", | |
| "affiliation": { | |
| "laboratory": "", | |
| "institution": "National Institute of Technology Silchar Assam", | |
| "location": { | |
| "country": "India" | |
| } | |
| }, | |
| "email": "" | |
| } | |
| ], | |
| "year": "", | |
| "venue": null, | |
| "identifiers": {}, | |
| "abstract": "Neural machine translation (NMT) is a widely accepted approach in the machine translation (MT) community, translating from one natural language to another natural language. Although, NMT shows remarkable performance in both high and low resource languages, it needs sufficient training corpus. The availability of a parallel corpus in low resource language pairs is one of the challenging tasks in MT. To mitigate this issue, NMT attempts to utilize a monolingual corpus to get better at translation for low resource language pairs. Workshop on Technologies for MT of Low Resource Languages (LoResMT 2020) organized shared tasks of low resource language pair translation using zero-shot NMT. Here, the parallel corpus is not used and only monolingual corpora is allowed. We have participated in the same shared task with our team name CNLP-NITS for the Russian-Hindi language pair. We have used masked sequence to sequence pre-training for language generation (MASS) with only monolingual corpus following the unsupervised NMT architecture. The evaluated results are declared at the LoResMT 2020 shared task, which reports that our system achieves the bilingual evaluation understudy (BLEU) score of 0.59, precision score of 3.43, recall score of 5.48, F-measure score of 4.22, and rank-based intuitive bilingual evaluation score (RIBES) of 0.180147 in Russian to Hindi translation. And for Hindi to Russian translation, we have achieved BLEU, precision, recall, F-measure, and RIBES score of 1.11, 4.72, 4.41, 4.56, and 0.026842 respectively.", | |
| "pdf_parse": { | |
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| "abstract": [ | |
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| "text": "Neural machine translation (NMT) is a widely accepted approach in the machine translation (MT) community, translating from one natural language to another natural language. Although, NMT shows remarkable performance in both high and low resource languages, it needs sufficient training corpus. The availability of a parallel corpus in low resource language pairs is one of the challenging tasks in MT. To mitigate this issue, NMT attempts to utilize a monolingual corpus to get better at translation for low resource language pairs. Workshop on Technologies for MT of Low Resource Languages (LoResMT 2020) organized shared tasks of low resource language pair translation using zero-shot NMT. Here, the parallel corpus is not used and only monolingual corpora is allowed. We have participated in the same shared task with our team name CNLP-NITS for the Russian-Hindi language pair. We have used masked sequence to sequence pre-training for language generation (MASS) with only monolingual corpus following the unsupervised NMT architecture. The evaluated results are declared at the LoResMT 2020 shared task, which reports that our system achieves the bilingual evaluation understudy (BLEU) score of 0.59, precision score of 3.43, recall score of 5.48, F-measure score of 4.22, and rank-based intuitive bilingual evaluation score (RIBES) of 0.180147 in Russian to Hindi translation. And for Hindi to Russian translation, we have achieved BLEU, precision, recall, F-measure, and RIBES score of 1.11, 4.72, 4.41, 4.56, and 0.026842 respectively.", | |
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| "text": "The end-to-end recurrent neural network (RNN) based NMT (Cho et al., 2014b,a) approach attracts attention in MT because it deals with many challenges like variable-length phrases using sequence to sequence learning concept, long-term dependency problem adopting long short term memory (LSTM) (Sutskever et al., 2014) , attention mechanism (Bahdanau et al., 2015; Luong et al., 2015) which pays attention globally and locally to all source words. The RNN based NMT approach is not able to process all the input words parallelly, to solve parallelization transformer-based NMT (Vaswani et al., 2017) is proposed by using a self-attention mechanism. Despite modifying NMT architecture, it needs reasonable parallel training data which is a challenge for low resource language pair translation. Generally, language pairs can be considered as low-resource when training data is less than a million (Kocmi, 2020) . For low resource language pair translation, pivot-based NMT (Kim et al., 2019) is an effective approach where an intermediate language is considered as a pivot language (source to pivot and pivot to target). (Johnson et al., 2017) introduced a zero-shot approach to language pair translation without considering the parallel data using multilingual-based NMT. In this paper, we have participated in the LoResMT 2020 shared task of zero-shot NMT approach on Russian-Hindi pair using the only monolingual corpus and the same has been implemented using MASS-based unsupervised NMT (Song et al., 2019) . The reason behind choosing MASS-based unsupervised NMT is that it achieves state-of-the-art performance on the unsupervised English-French pair translation.", | |
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| "section": "Introduction", | |
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| "text": "There is a lack of background work on Russian-Hindi translation. However, the literature survey finds work on unsupervised NMT using MASS (Song et al., 2019) which outperform previous unsupervised approaches (Lample and Conneau, 2019; Lample et al., 2018) . (Song et al., 2019) ", | |
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| "text": "without us- X 8 X 4 X 7 X 1 X 2 X 3 _ X 6 _ _ _ _ _ Encoder Decoder _ _ X 5", | |
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| "text": "Attention Figure 1 : The encoder-decoder framework of the MASS model used (as adopted from (Song et al., 2019) (Vaswani et al., 2017) based MASS implemented in two steps: pre-training on the monolingual data and then fine-tuning with the self-generated back translation data which acts as a pseudo bilingual corpus during the training process.", | |
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| "text": "The LoResMT 2020 shared task organizer (Ojha et al., 2020) provided the Russian-Hindi monolingual dataset of train, valid, and test sets, which is summarized in Table 1 . Additionally, we have used external monolingual data set of Hindi (9 GB) from IITB 1 (Kunchukuttan et al., 2018; Bojar et al., 2014) and Russian (9GB) from WMT16 2 .", | |
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| "text": "We have adopted MASS based unsupervised NMT (Song et al., 2019) to build our system on a single GPU. Our system consists of two major steps namely the pre-training and then the fine-tuning step which are discussed in the sub-sections 4.1 and 4.2. For BPE (Sennrich et al., 2016) and vocabulary creation, we have used the cross-language model (XLM) (Lample and Conneau, 2019) codebase as given in their repository 3 . Moses is used for tokenization (Koehn and Hoang, 2010) . The MASS (Song et al., 2019) based model leverages encode-decoder framework to develop complete sentences from given fractured pieces of sentences as shown in Figure 1 . The model details are further described in Section 4.1 and 4.2, where we have shown the pre-training and fine tuning step respectively.", | |
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| "text": "For the pre-training step, following (Song et al., 2019) we have undertaken the log likelihood objective function (LF ) as shown in Equation 1. Here, s belongs to the source sentence corpus S. And in a particular sentence s, the region from u to v is masked, such that the sentence length remains constant.", | |
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| "text": "LF (\u03b8; S) = 1 |S| \u03a3 s\u2208S log P (s u:v |s \\u:v ; \u03b8) = 1 |S| \u03a3 s\u2208S log v t=u P (s u:v t |s u:v <t , s \\u:v ; \u03b8).", | |
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| "text": "(1) Here, the seq2seq model learns the parameter \u03b8 to compute the conditional probability. t denotes the word position.", | |
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| "text": "Since, the parallel data is not made available by the LoResMT 2020 organizers for this specific task, we have undertaken the unsupervised approach as followed by (Song et al., 2019) . Only the monolingual data is used here. Here simply backtranslation is employed to generate pseudo bilin- ", | |
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| "text": "During pre-processing of the data, following (Song et al., 2019) and using the code provided by (Lample and Conneau, 2019), we used fastBPE 4 to learn byte pair encoding (BPE) vocabulary with 50,000 codes. Also, for leveraging the model features, we have followed the settings of (Song et al., 2019) . In the pre-training step, we have followed the default settings of Transformer model-based Mass (Song et al., 2019) , where 6 layers with 8 attention heads are used. Due to limited computational resources, we have used 256 embedding layers with batch size 32, tokens per batch 500 and dropout 0.1. The obtained pre-trained model from 4.1 are fine-tuned with pseudo bilingual corpus through self-generated back-translation data following default settings of (Song et al., 2019) .", | |
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| "text": "The LoResMT 2020 shared task organizer declared the evaluation result 5 of zero-shot NMT on the language pairs namely, Hindi-Bhojpuri, Hindi-Magahi, and Russian-Hindi, and participated by two teams only. For the Russian-Hindi language pair, only our team participated and our team name is CNLP-NITS. The results are evaluated using automatic evaluation metrics, BLEU (Papineni et al., 2002) , precision, recall, F-measure and RIBES (Isozaki et al., 2010) . We have submitted two systems result, one only using provided monolingual data (extension -a) and another with external monolingual data addition of provided monolingual data (extension -c) and the same have been reported in Table 2 . From Table 2 , it is observed that our scores are very low. However, it is to be noted that with increasing monolingual data, the performance of our systems improves. Moreover, from the predicted translation as shown in Figure 2 , it is quite clear that the translation accuracy is very poor in terms of adequacy but better in the fluency factor of translation. To achieve better translation accuracy, we need to improve both adequacy as well as fluency of predicted translations. In this work, we have used the default tokenizer i.e. Moses. In future, we will use IndicNLP tokenizer (Kunchukuttan, 2020) . This tokenizer is specifically designed for Indic languages, in order to improve the overall performance of predictive models in Hindi languages.", | |
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| "section": "Result and Analysis", | |
| "sec_num": "6" | |
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| { | |
| "text": "This paper presents a zero-shot NMT task on the Russian \u21d4 Hindi translation, this system was used to participate in the LoResMT 2020 shared task. We have used unsupervised NMT approach of MASS (Song et al., 2019) to build a single model that can translate in both the directions i.e. Russian to Hindi and vice-versa. The obtained scores and closely observed predicted output remarks that our future works require significant improvement to achieve better translation accuracies in both directions.", | |
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| "section": "Conclusion and Future Work", | |
| "sec_num": "7" | |
| }, | |
| { | |
| "text": "http://www.cfilt.iitb.ac.in/iitb_ parallel/ 2 http://www.statmt.org/wmt16/ translation-task.html", | |
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| "text": "https://github.com/facebookresearch/ XLM", | |
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| "text": "https://github.com/glample/fastBPE 5 https://bit.ly/3l2Hc1h", | |
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| { | |
| "text": "We would like to thank Center for Natural Language Processing (CNLP) and Department of Computer Science and Engineering at National Institute of Technology, Silchar, India for providing the requisite support and infrastructure to execute this work. Also, thank to LoResMT 2020 shared task organizers.", | |
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| "section": "Acknowledgement", | |
| "sec_num": null | |
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| ], | |
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| "BIBREF0": { | |
| "ref_id": "b0", | |
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