| { |
| "paper_id": "2020", |
| "header": { |
| "generated_with": "S2ORC 1.0.0", |
| "date_generated": "2023-01-19T04:33:43.521737Z" |
| }, |
| "title": "NLPRL Odia-English: Indic Language Neural Machine Translation System", |
| "authors": [ |
| { |
| "first": "Rupjyoti", |
| "middle": [], |
| "last": "Baruah", |
| "suffix": "", |
| "affiliation": { |
| "laboratory": "", |
| "institution": "Indian Institute Technology (BHU) Varanasi", |
| "location": { |
| "country": "India" |
| } |
| }, |
| "email": "rupjyotibaruah.rs.cse18@iitbhu.ac.in" |
| }, |
| { |
| "first": "Rajesh", |
| "middle": [], |
| "last": "Kumar Mundotiya", |
| "suffix": "", |
| "affiliation": { |
| "laboratory": "", |
| "institution": "Indian Institute Technology (BHU) Varanasi", |
| "location": { |
| "country": "India" |
| } |
| }, |
| "email": "" |
| } |
| ], |
| "year": "", |
| "venue": null, |
| "identifiers": {}, |
| "abstract": "In this paper, we (team name is NLPRL) describe our systems that were submitted to the translation shared tasks at WAT 2020. We submitted two systems Odia to English and English to Odia translation under Indic tasks. We presented the Neural Machine Translation (NMT) system based on the Transformer approach using a byte-level version of BPE, which requires a base vocabulary of size 256 only. Experiments show the BLEU score of English to Odia 1.34 and Odia to English 11.33 on the benchmark test data.", |
| "pdf_parse": { |
| "paper_id": "2020", |
| "_pdf_hash": "", |
| "abstract": [ |
| { |
| "text": "In this paper, we (team name is NLPRL) describe our systems that were submitted to the translation shared tasks at WAT 2020. We submitted two systems Odia to English and English to Odia translation under Indic tasks. We presented the Neural Machine Translation (NMT) system based on the Transformer approach using a byte-level version of BPE, which requires a base vocabulary of size 256 only. Experiments show the BLEU score of English to Odia 1.34 and Odia to English 11.33 on the benchmark test data.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Abstract", |
| "sec_num": null |
| } |
| ], |
| "body_text": [ |
| { |
| "text": "In this paper, we (team name is NLPRL) describe the system that we develop as part of our participation in the Workshop on Asian Translation (WAT) 2020 (Nakazawa et al., 2019 (Nakazawa et al., , 2020 for the Odia-English language pairs. For the first time, Odia-English language pair is adopted for a translation task in the WAT 2020. The shared task organizers provide Odia-English (OdiEnCorp 2.0) parallel corpus that contains train, valid, and test data collected by researchers at UFAL (Institute of Formal and Applied Linguistics). The Odia (old name is Oriya) language is one of the official languages of Odisha, situated in the eastern part of India. It belongs to the Eastern Indo-Aryan group, which is a branch of Indu-European languages. It comes under one of the 22 official languages recognized by the Government of India.", |
| "cite_spans": [ |
| { |
| "start": 152, |
| "end": 174, |
| "text": "(Nakazawa et al., 2019", |
| "ref_id": "BIBREF5" |
| }, |
| { |
| "start": 175, |
| "end": 199, |
| "text": "(Nakazawa et al., , 2020", |
| "ref_id": "BIBREF6" |
| } |
| ], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Introduction", |
| "sec_num": "1" |
| }, |
| { |
| "text": "In this experiment, we use byte-level subwords, specifically byte-level BPE (BBPE) (Wang et al., 2020) tokenizer, which is more efficient than pure bytes, worked on the Transformer architecture. The primary motivation was to try out this model on Odia-English parallel data provided by the organizers. We presented the translation results by using an automatic evaluation server prepared by shared task technical collaborators.", |
| "cite_spans": [ |
| { |
| "start": 83, |
| "end": 102, |
| "text": "(Wang et al., 2020)", |
| "ref_id": "BIBREF12" |
| } |
| ], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Introduction", |
| "sec_num": "1" |
| }, |
| { |
| "text": "The article is structured as follows. In Section 2, we discuss our system description that covering the statistics of the dataset and its preparation with the experimental setup. Section 3 describes results and analysis. Finally, We conclude in Section 4 with the conclusion and future work.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Introduction", |
| "sec_num": "1" |
| }, |
| { |
| "text": "ANUVADAKSH 1 , an expert in English to Indian Languages Machine Translation System (EILMT), allows translation of the text from English to eight Indian languages. The EILMT tool is a hybrid system that has been trained with the data in the three different domains of health, tourism, and agriculture. The EILMT can translate English to Odia language in all three domains 2 . The machine translation system, OMTrans 3 as English to Oriya by Utkal University, Bhuvaneshwar using school book sentences (product information only). The OM-Trans system translates text from English to Oriya based on grammar and semantics of the language.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Related Study", |
| "sec_num": "2" |
| }, |
| { |
| "text": "There are many challenges for Neural Machine Translation (NMT) than other approaches mentioned in different literature (Koehn and Knowles, 2017) . Most state-of-the-art NMT systems achieve outstanding results based on large parallel corpora only. It has been outperforming the traditional methods for machine translation, such as rule-based and statistical-based approaches. However, for the low resource languages, various techniques have been proposed for NMT, such as metalearning (Gu et al., 2018) , transfer learning (Zoph et al., 2016) , which produced promising results. The two main strategies, namely pivot-based and zero-shot machine translation, were used to build NMT models without direct parallel data.", |
| "cite_spans": [ |
| { |
| "start": 119, |
| "end": 144, |
| "text": "(Koehn and Knowles, 2017)", |
| "ref_id": "BIBREF4" |
| }, |
| { |
| "start": 484, |
| "end": 501, |
| "text": "(Gu et al., 2018)", |
| "ref_id": "BIBREF1" |
| }, |
| { |
| "start": 522, |
| "end": 541, |
| "text": "(Zoph et al., 2016)", |
| "ref_id": "BIBREF13" |
| } |
| ], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Related Study", |
| "sec_num": "2" |
| }, |
| { |
| "text": "The performance of NMT on the low resource languages mainly suffers due to the Out-Of-Vocabulary (OOV), the words not in the trained NMT model's vocabulary.. NMT models typically operate with a fixed vocabulary; however, the translation is an open-vocabulary problem. Several approaches have been proposed to reduce the issue of OOV. Byte-Pair-Encoding (BPE) (Sennrich et al., 2016) enables NMT model translation on openvocabulary by encoding rare and unknown words as a sequence of subword units. Wang et al. (2020) proposed BBPE, which builds a Byte-level subword vocabulary for machine translation.", |
| "cite_spans": [ |
| { |
| "start": 359, |
| "end": 382, |
| "text": "(Sennrich et al., 2016)", |
| "ref_id": "BIBREF10" |
| }, |
| { |
| "start": 498, |
| "end": 516, |
| "text": "Wang et al. (2020)", |
| "ref_id": "BIBREF12" |
| } |
| ], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Related Study", |
| "sec_num": "2" |
| }, |
| { |
| "text": "This section covers a dataset, preprocessing, and the experimental setup required for our systems.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "System Description", |
| "sec_num": "3" |
| }, |
| { |
| "text": "To the best of our knowledge, this is the first time the language pair (Odia-English) is running as a shared task in any machine translation competition. OdiEnCorp 2.0 parallel corpus covers many domains, namely the Bible, different literature, Government policies, general conversation, and many topics in Wiki data. The training data includes 69370 lines of parallel corpora. The validation and test data contain 13544 and 14344 lines of parallel sentences, respectively, for both directional pairs (Odia-English).", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Dataset", |
| "sec_num": "3.1" |
| }, |
| { |
| "text": "The standard fairseq preprocess 4 script has been exploited for performing the tokenization that uses MOSES tokenizer. The lowercasing operation has performed over the input sentences before performing the tokenization. These tokens are further utilized for performing the BPE encoding because of BBPE encoding composed on BPE. The size of BBPE is 2048.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Preprocessing", |
| "sec_num": "3.2" |
| }, |
| { |
| "text": "The Transformer translation model proposed by (Vaswani et al., 2017) , which relies on selfattention mechanisms by handling long-term dependencies, has achieved state-of-the-art performance in recent NMT tasks. The vocabulary of the source and target language have been shared during the training of the model. The model uses 0.3 as a dropout to regularize the model. The model trained by Adam optimizer (Kingma and Ba, 2014) with 0.001 as the initial learning rate, which further gradually decays after each 4000.", |
| "cite_spans": [ |
| { |
| "start": 46, |
| "end": 68, |
| "text": "(Vaswani et al., 2017)", |
| "ref_id": "BIBREF11" |
| } |
| ], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Experimental Setup", |
| "sec_num": "3.3" |
| }, |
| { |
| "text": "We conduct experiments using fairseq (Ott et al., 2019) library, a sequence modeling toolkit to train our model with the same value for remaining parameters and hyper-parameters as mentioned in the original paper.", |
| "cite_spans": [ |
| { |
| "start": 37, |
| "end": 55, |
| "text": "(Ott et al., 2019)", |
| "ref_id": "BIBREF7" |
| } |
| ], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Experimental Setup", |
| "sec_num": "3.3" |
| }, |
| { |
| "text": "The organizers have evaluated the submitted predictions on the test set (of OdiEnCorp 2.0 parallel corpus) of both directions pair in terms of BiLingual Evaluation Understudy (BLEU) (Papineni et al., 2002) , Rank-based Intuitive Bilingual Evaluation Score (RIBES) (Isozaki et al., 2010) and Adequacy-Fluency Metrics (AM-FM) (Banchs et al., 2015). Table 1 and Table 2 depict the BLEU score and RIBES of the NLPRL and Organizer's model, trained on both directions, respectively. The obtained scores by baseline model and our model have referred to as Organizer and NLPRL in the following tables. From these tables, we have inferred that our model performs better for Odia\u2192English, compared to the baseline. The Figure 1 also indicates that the NLPRL model obtained adequate and fluent results for Odia\u2192English, while vice-versa, it is not true.", |
| "cite_spans": [ |
| { |
| "start": 182, |
| "end": 205, |
| "text": "(Papineni et al., 2002)", |
| "ref_id": "BIBREF8" |
| }, |
| { |
| "start": 264, |
| "end": 286, |
| "text": "(Isozaki et al., 2010)", |
| "ref_id": "BIBREF2" |
| } |
| ], |
| "ref_spans": [ |
| { |
| "start": 347, |
| "end": 366, |
| "text": "Table 1 and Table 2", |
| "ref_id": "TABREF0" |
| }, |
| { |
| "start": 709, |
| "end": 717, |
| "text": "Figure 1", |
| "ref_id": "FIGREF0" |
| } |
| ], |
| "eq_spans": [], |
| "section": "Result and Analysis", |
| "sec_num": "4" |
| }, |
| { |
| "text": "Organizer NLPRL Odia\u2192English 8.93 11.33 English\u2192Odia 5.49 1.34 ", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Pair", |
| "sec_num": null |
| }, |
| { |
| "text": "In this paper, we report our submitted system scores based on the official scores released by the WAT 2020 shared translation task. We train our system for Odia-to-English and English-to-Odia language pairs using the Transformer-based neural machine translation using a byte-level version of BPE. WAT 2020 depicts three types of scores, namely BLEU, RIBES, and AM-FM. The BLEU score, a standard metric for machine translation evaluation of English to Odia and Odia to English on the benchmark test data, is 1.34 and 11.33, respectively. We found that our model performs better for Odia\u2192English, compared to the Organizer's model. As the next step, we would like to improve the evaluation score using a multi-lingual NMT model. Furthermore, using monolingual source and target language can improve the translation score of both directions of the language pair.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Conclusion and Future Work", |
| "sec_num": "5" |
| }, |
| { |
| "text": "https://cdac.in/index.aspx?id=mc_mat_ anuvadaksha 2 http://eilmt.rb-aai.in/ 3 http://www.ilts-utkal.org/omt.htm", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "", |
| "sec_num": null |
| }, |
| { |
| "text": "https://github.com/pytorch/fairseq", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "", |
| "sec_num": null |
| } |
| ], |
| "back_matter": [ |
| { |
| "text": "The support and the resources provided by PARAM Shivay Facility under the National Supercomputing Mission, Government of India at the Indian Institute of Technology, Varanasi are gratefully acknowledged.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Acknowledgments", |
| "sec_num": null |
| } |
| ], |
| "bib_entries": { |
| "BIBREF0": { |
| "ref_id": "b0", |
| "title": "Adequacy-Fluency Metrics: Evaluating MT in the Continuous Space Model Framework", |
| "authors": [ |
| { |
| "first": "E", |
| "middle": [], |
| "last": "Rafael", |
| "suffix": "" |
| }, |
| { |
| "first": "", |
| "middle": [], |
| "last": "Banchs", |
| "suffix": "" |
| }, |
| { |
| "first": "F D'", |
| "middle": [], |
| "last": "Luis", |
| "suffix": "" |
| }, |
| { |
| "first": "Haizhou", |
| "middle": [], |
| "last": "Haro", |
| "suffix": "" |
| }, |
| { |
| "first": "", |
| "middle": [], |
| "last": "Li", |
| "suffix": "" |
| } |
| ], |
| "year": 2015, |
| "venue": "IEEE/ACM Transactions on Audio, Speech and Language Processing (TASLP)", |
| "volume": "23", |
| "issue": "3", |
| "pages": "472--482", |
| "other_ids": {}, |
| "num": null, |
| "urls": [], |
| "raw_text": "Rafael E Banchs, Luis F D'Haro, and Haizhou Li. 2015. Adequacy-Fluency Metrics: Evaluating MT in the Continuous Space Model Framework. IEEE/ACM Transactions on Audio, Speech and Language Pro- cessing (TASLP), 23(3):472-482.", |
| "links": null |
| }, |
| "BIBREF1": { |
| "ref_id": "b1", |
| "title": "Meta-Learning for Low-Resource Neural Machine Translation", |
| "authors": [ |
| { |
| "first": "Jiatao", |
| "middle": [], |
| "last": "Gu", |
| "suffix": "" |
| }, |
| { |
| "first": "Yong", |
| "middle": [], |
| "last": "Wang", |
| "suffix": "" |
| }, |
| { |
| "first": "Yun", |
| "middle": [], |
| "last": "Chen", |
| "suffix": "" |
| }, |
| { |
| "first": "O", |
| "middle": [ |
| "K" |
| ], |
| "last": "Victor", |
| "suffix": "" |
| }, |
| { |
| "first": "Kyunghyun", |
| "middle": [], |
| "last": "Li", |
| "suffix": "" |
| }, |
| { |
| "first": "", |
| "middle": [], |
| "last": "Cho", |
| "suffix": "" |
| } |
| ], |
| "year": 2018, |
| "venue": "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing", |
| "volume": "", |
| "issue": "", |
| "pages": "3622--3631", |
| "other_ids": {}, |
| "num": null, |
| "urls": [], |
| "raw_text": "Jiatao Gu, Yong Wang, Yun Chen, Victor OK Li, and Kyunghyun Cho. 2018. Meta-Learning for Low- Resource Neural Machine Translation. In Proceed- ings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 3622-3631.", |
| "links": null |
| }, |
| "BIBREF2": { |
| "ref_id": "b2", |
| "title": "Automatic Evaluation of Translation Quality for Distant Language Pairs", |
| "authors": [ |
| { |
| "first": "Hideki", |
| "middle": [], |
| "last": "Isozaki", |
| "suffix": "" |
| }, |
| { |
| "first": "Tsutomu", |
| "middle": [], |
| "last": "Hirao", |
| "suffix": "" |
| }, |
| { |
| "first": "Kevin", |
| "middle": [], |
| "last": "Duh", |
| "suffix": "" |
| }, |
| { |
| "first": "Katsuhito", |
| "middle": [], |
| "last": "Sudoh", |
| "suffix": "" |
| }, |
| { |
| "first": "Hajime", |
| "middle": [], |
| "last": "Tsukada", |
| "suffix": "" |
| } |
| ], |
| "year": 2010, |
| "venue": "Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing", |
| "volume": "", |
| "issue": "", |
| "pages": "944--952", |
| "other_ids": {}, |
| "num": null, |
| "urls": [], |
| "raw_text": "Hideki Isozaki, Tsutomu Hirao, Kevin Duh, Katsuhito Sudoh, and Hajime Tsukada. 2010. Automatic Eval- uation of Translation Quality for Distant Language Pairs. In Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing, pages 944-952.", |
| "links": null |
| }, |
| "BIBREF3": { |
| "ref_id": "b3", |
| "title": "Adam: A Method for Stochastic Optimization", |
| "authors": [ |
| { |
| "first": "P", |
| "middle": [], |
| "last": "Diederik", |
| "suffix": "" |
| }, |
| { |
| "first": "Jimmy", |
| "middle": [], |
| "last": "Kingma", |
| "suffix": "" |
| }, |
| { |
| "first": "", |
| "middle": [], |
| "last": "Ba", |
| "suffix": "" |
| } |
| ], |
| "year": 2014, |
| "venue": "", |
| "volume": "", |
| "issue": "", |
| "pages": "", |
| "other_ids": { |
| "arXiv": [ |
| "arXiv:1412.6980" |
| ] |
| }, |
| "num": null, |
| "urls": [], |
| "raw_text": "Diederik P Kingma and Jimmy Ba. 2014. Adam: A Method for Stochastic Optimization. arXiv preprint arXiv:1412.6980.", |
| "links": null |
| }, |
| "BIBREF4": { |
| "ref_id": "b4", |
| "title": "Six Challenges for Neural Machine Translation", |
| "authors": [ |
| { |
| "first": "Philipp", |
| "middle": [], |
| "last": "Koehn", |
| "suffix": "" |
| }, |
| { |
| "first": "Rebecca", |
| "middle": [], |
| "last": "Knowles", |
| "suffix": "" |
| } |
| ], |
| "year": 2017, |
| "venue": "Proceedings of the First Workshop on Neural Machine Translation", |
| "volume": "", |
| "issue": "", |
| "pages": "28--39", |
| "other_ids": {}, |
| "num": null, |
| "urls": [], |
| "raw_text": "Philipp Koehn and Rebecca Knowles. 2017. Six Chal- lenges for Neural Machine Translation. In Proceed- ings of the First Workshop on Neural Machine Trans- lation, pages 28-39.", |
| "links": null |
| }, |
| "BIBREF5": { |
| "ref_id": "b5", |
| "title": "Overview of the 6th Workshop on Asian Translation", |
| "authors": [ |
| { |
| "first": "Toshiaki", |
| "middle": [], |
| "last": "Nakazawa", |
| "suffix": "" |
| }, |
| { |
| "first": "Nobushige", |
| "middle": [], |
| "last": "Doi", |
| "suffix": "" |
| }, |
| { |
| "first": "Shohei", |
| "middle": [], |
| "last": "Higashiyama", |
| "suffix": "" |
| }, |
| { |
| "first": "Chenchen", |
| "middle": [], |
| "last": "Ding", |
| "suffix": "" |
| }, |
| { |
| "first": "Raj", |
| "middle": [], |
| "last": "Dabre", |
| "suffix": "" |
| }, |
| { |
| "first": "Hideya", |
| "middle": [], |
| "last": "Mino", |
| "suffix": "" |
| }, |
| { |
| "first": "Isao", |
| "middle": [], |
| "last": "Goto", |
| "suffix": "" |
| }, |
| { |
| "first": "Win", |
| "middle": [ |
| "Pa" |
| ], |
| "last": "Pa", |
| "suffix": "" |
| }, |
| { |
| "first": "Anoop", |
| "middle": [], |
| "last": "Kunchukuttan", |
| "suffix": "" |
| }, |
| { |
| "first": "Shantipriya", |
| "middle": [], |
| "last": "Parida", |
| "suffix": "" |
| } |
| ], |
| "year": 2019, |
| "venue": "Proceedings of the 6th Workshop on Asian Translation", |
| "volume": "", |
| "issue": "", |
| "pages": "1--35", |
| "other_ids": {}, |
| "num": null, |
| "urls": [], |
| "raw_text": "Toshiaki Nakazawa, Nobushige Doi, Shohei Hi- gashiyama, Chenchen Ding, Raj Dabre, Hideya Mino, Isao Goto, Win Pa Pa, Anoop Kunchukuttan, Shantipriya Parida, et al. 2019. Overview of the 6th Workshop on Asian Translation. In Proceedings of the 6th Workshop on Asian Translation, pages 1-35.", |
| "links": null |
| }, |
| "BIBREF6": { |
| "ref_id": "b6", |
| "title": "Overview of the 7th workshop on Asian translation", |
| "authors": [ |
| { |
| "first": "Toshiaki", |
| "middle": [], |
| "last": "Nakazawa", |
| "suffix": "" |
| }, |
| { |
| "first": "Hideki", |
| "middle": [], |
| "last": "Nakayama", |
| "suffix": "" |
| }, |
| { |
| "first": "Chenchen", |
| "middle": [], |
| "last": "Ding", |
| "suffix": "" |
| }, |
| { |
| "first": "Raj", |
| "middle": [], |
| "last": "Dabre", |
| "suffix": "" |
| }, |
| { |
| "first": "Hideya", |
| "middle": [], |
| "last": "Mino", |
| "suffix": "" |
| }, |
| { |
| "first": "Isao", |
| "middle": [], |
| "last": "Goto", |
| "suffix": "" |
| }, |
| { |
| "first": "Win", |
| "middle": [ |
| "Pa" |
| ], |
| "last": "Pa", |
| "suffix": "" |
| }, |
| { |
| "first": "Anoop", |
| "middle": [], |
| "last": "Kunchukuttan", |
| "suffix": "" |
| }, |
| { |
| "first": "Shantipriya", |
| "middle": [], |
| "last": "Parida", |
| "suffix": "" |
| }, |
| { |
| "first": "Ond\u0159ej", |
| "middle": [], |
| "last": "Bojar", |
| "suffix": "" |
| }, |
| { |
| "first": "Sadao", |
| "middle": [], |
| "last": "Kurohashi", |
| "suffix": "" |
| } |
| ], |
| "year": 2020, |
| "venue": "Proceedings of the 7th Workshop on Asian Translation", |
| "volume": "", |
| "issue": "", |
| "pages": "", |
| "other_ids": {}, |
| "num": null, |
| "urls": [], |
| "raw_text": "Toshiaki Nakazawa, Hideki Nakayama, Chenchen Ding, Raj Dabre, Hideya Mino, Isao Goto, Win Pa Pa, Anoop Kunchukuttan, Shantipriya Parida, Ond\u0159ej Bojar, and Sadao Kurohashi. 2020. Overview of the 7th workshop on Asian transla- tion. In Proceedings of the 7th Workshop on Asian Translation, Suzhou, China. Association for Compu- tational Linguistics.", |
| "links": null |
| }, |
| "BIBREF7": { |
| "ref_id": "b7", |
| "title": "FAIRSEQ: A Fast, Extensible Toolkit for Sequence Modeling", |
| "authors": [ |
| { |
| "first": "Myle", |
| "middle": [], |
| "last": "Ott", |
| "suffix": "" |
| }, |
| { |
| "first": "Sergey", |
| "middle": [], |
| "last": "Edunov", |
| "suffix": "" |
| }, |
| { |
| "first": "Alexei", |
| "middle": [], |
| "last": "Baevski", |
| "suffix": "" |
| }, |
| { |
| "first": "Angela", |
| "middle": [], |
| "last": "Fan", |
| "suffix": "" |
| }, |
| { |
| "first": "Sam", |
| "middle": [], |
| "last": "Gross", |
| "suffix": "" |
| }, |
| { |
| "first": "Nathan", |
| "middle": [], |
| "last": "Ng", |
| "suffix": "" |
| }, |
| { |
| "first": "David", |
| "middle": [], |
| "last": "Grangier", |
| "suffix": "" |
| }, |
| { |
| "first": "Michael", |
| "middle": [], |
| "last": "Auli", |
| "suffix": "" |
| } |
| ], |
| "year": 2019, |
| "venue": "Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics (Demonstrations)", |
| "volume": "", |
| "issue": "", |
| "pages": "48--53", |
| "other_ids": {}, |
| "num": null, |
| "urls": [], |
| "raw_text": "Myle Ott, Sergey Edunov, Alexei Baevski, Angela Fan, Sam Gross, Nathan Ng, David Grangier, and Michael Auli. 2019. FAIRSEQ: A Fast, Extensible Toolkit for Sequence Modeling. In Proceedings of the 2019 Conference of the North American Chap- ter of the Association for Computational Linguistics (Demonstrations), pages 48-53.", |
| "links": null |
| }, |
| "BIBREF8": { |
| "ref_id": "b8", |
| "title": "BLEU: a Method for Automatic Evaluation of Machine Translation", |
| "authors": [ |
| { |
| "first": "Kishore", |
| "middle": [], |
| "last": "Papineni", |
| "suffix": "" |
| }, |
| { |
| "first": "Salim", |
| "middle": [], |
| "last": "Roukos", |
| "suffix": "" |
| }, |
| { |
| "first": "Todd", |
| "middle": [], |
| "last": "Ward", |
| "suffix": "" |
| }, |
| { |
| "first": "Wei-Jing", |
| "middle": [], |
| "last": "Zhu", |
| "suffix": "" |
| } |
| ], |
| "year": 2002, |
| "venue": "Proceedings of the 40th annual meeting of the Association for Computational Linguistics", |
| "volume": "", |
| "issue": "", |
| "pages": "311--318", |
| "other_ids": {}, |
| "num": null, |
| "urls": [], |
| "raw_text": "Kishore Papineni, Salim Roukos, Todd Ward, and Wei- Jing Zhu. 2002. BLEU: a Method for Automatic Evaluation of Machine Translation. In Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pages 311-318.", |
| "links": null |
| }, |
| "BIBREF9": { |
| "ref_id": "b9", |
| "title": "OdiEnCorp 2.0: Odia-English Parallel Corpus for Machine Translation", |
| "authors": [ |
| { |
| "first": "Shantipriya", |
| "middle": [], |
| "last": "Parida", |
| "suffix": "" |
| }, |
| { |
| "first": "Ond\u0159ej", |
| "middle": [], |
| "last": "Satya Ranjan Dash", |
| "suffix": "" |
| }, |
| { |
| "first": "Petr", |
| "middle": [], |
| "last": "Bojar", |
| "suffix": "" |
| }, |
| { |
| "first": "Priyanka", |
| "middle": [], |
| "last": "Motlicek", |
| "suffix": "" |
| }, |
| { |
| "first": "Debasish", |
| "middle": [], |
| "last": "Pattnaik", |
| "suffix": "" |
| } |
| ], |
| "year": 2020, |
| "venue": "Proceedings of the WILDRE5-5th Workshop on Indian Language Data: Resources and Evaluation", |
| "volume": "", |
| "issue": "", |
| "pages": "14--19", |
| "other_ids": {}, |
| "num": null, |
| "urls": [], |
| "raw_text": "Shantipriya Parida, Satya Ranjan Dash, Ond\u0159ej Bojar, Petr Motlicek, Priyanka Pattnaik, and Debasish Ku- mar Mallick. 2020. OdiEnCorp 2.0: Odia-English Parallel Corpus for Machine Translation. In Pro- ceedings of the WILDRE5-5th Workshop on Indian Language Data: Resources and Evaluation, pages 14-19.", |
| "links": null |
| }, |
| "BIBREF10": { |
| "ref_id": "b10", |
| "title": "Neural Machine Translation of Rare Words with Subword Units", |
| "authors": [ |
| { |
| "first": "Rico", |
| "middle": [], |
| "last": "Sennrich", |
| "suffix": "" |
| }, |
| { |
| "first": "Barry", |
| "middle": [], |
| "last": "Haddow", |
| "suffix": "" |
| }, |
| { |
| "first": "Alexandra", |
| "middle": [], |
| "last": "Birch", |
| "suffix": "" |
| } |
| ], |
| "year": 2016, |
| "venue": "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics", |
| "volume": "1", |
| "issue": "", |
| "pages": "1715--1725", |
| "other_ids": {}, |
| "num": null, |
| "urls": [], |
| "raw_text": "Rico Sennrich, Barry Haddow, and Alexandra Birch. 2016. Neural Machine Translation of Rare Words with Subword Units. In Proceedings of the 54th An- nual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 1715- 1725.", |
| "links": null |
| }, |
| "BIBREF11": { |
| "ref_id": "b11", |
| "title": "Attention Is All You Need", |
| "authors": [ |
| { |
| "first": "Ashish", |
| "middle": [], |
| "last": "Vaswani", |
| "suffix": "" |
| }, |
| { |
| "first": "Noam", |
| "middle": [], |
| "last": "Shazeer", |
| "suffix": "" |
| }, |
| { |
| "first": "Niki", |
| "middle": [], |
| "last": "Parmar", |
| "suffix": "" |
| }, |
| { |
| "first": "Jakob", |
| "middle": [], |
| "last": "Uszkoreit", |
| "suffix": "" |
| }, |
| { |
| "first": "Llion", |
| "middle": [], |
| "last": "Jones", |
| "suffix": "" |
| }, |
| { |
| "first": "Aidan", |
| "middle": [ |
| "N" |
| ], |
| "last": "Gomez", |
| "suffix": "" |
| }, |
| { |
| "first": "\u0141ukasz", |
| "middle": [], |
| "last": "Kaiser", |
| "suffix": "" |
| }, |
| { |
| "first": "Illia", |
| "middle": [], |
| "last": "Polosukhin", |
| "suffix": "" |
| } |
| ], |
| "year": 2017, |
| "venue": "Advances in neural information processing systems", |
| "volume": "", |
| "issue": "", |
| "pages": "5998--6008", |
| "other_ids": {}, |
| "num": null, |
| "urls": [], |
| "raw_text": "Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, \u0141ukasz Kaiser, and Illia Polosukhin. 2017. Attention Is All You Need. In Advances in neural information pro- cessing systems, pages 5998-6008.", |
| "links": null |
| }, |
| "BIBREF12": { |
| "ref_id": "b12", |
| "title": "Neural Machine Translation with Byte-Level Subwords", |
| "authors": [ |
| { |
| "first": "Changhan", |
| "middle": [], |
| "last": "Wang", |
| "suffix": "" |
| }, |
| { |
| "first": "Kyunghyun", |
| "middle": [], |
| "last": "Cho", |
| "suffix": "" |
| }, |
| { |
| "first": "Jiatao", |
| "middle": [], |
| "last": "Gu", |
| "suffix": "" |
| } |
| ], |
| "year": 2020, |
| "venue": "The Thirty-Second Innovative Applications of Artificial Intelligence Conference", |
| "volume": "2020", |
| "issue": "", |
| "pages": "9154--9160", |
| "other_ids": {}, |
| "num": null, |
| "urls": [], |
| "raw_text": "Changhan Wang, Kyunghyun Cho, and Jiatao Gu. 2020. Neural Machine Translation with Byte-Level Sub- words. In The Thirty-Fourth AAAI Conference on Artificial Intelligence, AAAI 2020, The Thirty- Second Innovative Applications of Artificial Intelli- gence Conference, IAAI 2020, The Tenth AAAI Sym- posium on Educational Advances in Artificial Intel- ligence, EAAI 2020, New York, NY, USA, February 7-12, 2020, pages 9154-9160. AAAI Press.", |
| "links": null |
| }, |
| "BIBREF13": { |
| "ref_id": "b13", |
| "title": "Transfer Learning for Low-Resource Neural Machine Translation", |
| "authors": [ |
| { |
| "first": "Barret", |
| "middle": [], |
| "last": "Zoph", |
| "suffix": "" |
| }, |
| { |
| "first": "Deniz", |
| "middle": [], |
| "last": "Yuret", |
| "suffix": "" |
| }, |
| { |
| "first": "Jonathan", |
| "middle": [], |
| "last": "May", |
| "suffix": "" |
| }, |
| { |
| "first": "Kevin", |
| "middle": [], |
| "last": "Knight", |
| "suffix": "" |
| } |
| ], |
| "year": 2016, |
| "venue": "Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing", |
| "volume": "", |
| "issue": "", |
| "pages": "1568--1575", |
| "other_ids": {}, |
| "num": null, |
| "urls": [], |
| "raw_text": "Barret Zoph, Deniz Yuret, Jonathan May, and Kevin Knight. 2016. Transfer Learning for Low-Resource Neural Machine Translation. In Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, pages 1568-1575.", |
| "links": null |
| } |
| }, |
| "ref_entries": { |
| "FIGREF0": { |
| "uris": null, |
| "text": "Comparison of average AM, FM and AM-FM scores of the Organizer and NLPRL's model", |
| "type_str": "figure", |
| "num": null |
| }, |
| "TABREF0": { |
| "html": null, |
| "text": "BLEU score of Odia-English", |
| "type_str": "table", |
| "content": "<table><tr><td>Pair</td><td colspan=\"2\">Organizer NLPRL</td></tr><tr><td>Odia\u2192English</td><td>.349459</td><td>.462557</td></tr><tr><td>English\u2192Odia</td><td>.326116</td><td>.288520</td></tr></table>", |
| "num": null |
| }, |
| "TABREF1": { |
| "html": null, |
| "text": "RIBES score of Odia-English", |
| "type_str": "table", |
| "content": "<table/>", |
| "num": null |
| } |
| } |
| } |
| } |
