Low Resource Neural Machine Translation: Assamese to/from Other Indo-Aryan (Indic) Languages
Machine translation SMT NMT low resource Assamese Indo-Aryan sequence-to-sequence Transformer finetuned Transformer
Uttar-pradesh INDIA View Profile Rupjyoti Baruah Rajesh Kumar Mundotiya Anil Kumar Singh MundotiyaRajesh Kumar SinghAnil Kumar
Machine translation (MT) systems have been built using numerous different techniques
for bridging the language barriers. These techniques are broadly categorized into
approaches like Statistical Machine Translation (SMT) and Neural Machine Translation
(...
/pb-assets/head-metadata/apple-touch-icon-1574252172393.png
ACM Transactions on Asian and Low-Resource Language Information Processing
Abstract

Machine translation (MT) systems have been built using numerous different techniques for bridging the language barriers. These techniques are broadly categorized into approaches like Statistical Machine Translation (SMT) and Neural Machine Translation (NMT). End-to-end NMT systems significantly outperform SMT in translation quality on many language pairs, especially those with the adequate parallel corpus. We report comparative experiments on baseline MT systems for Assamese to other Indo-Aryan languages (in both translation directions) using the traditional Phrase-Based SMT as well as some more successful NMT architectures, namely basic sequence-to-sequence model with attention, Transformer, and finetuned Transformer. The results are evaluated using the most prominent and popular standard automatic metric BLEU (BiLingual Evaluation Understudy), as well as other well-known metrics for exploring the performance of different baseline MT systems, since this is the first such work involving Assamese. The evaluation scores are compared for SMT and NMT models for the effectiveness of bi-directional language pairs involving Assamese and other Indo-Aryan languages (Bangla, Gujarati, Hindi, Marathi, Odia, Sinhalese, and Urdu). The highest BLEU scores obtained are for Assamese to Sinhalese for SMT (35.63) and the Assamese to Bangla for NMT systems (seq2seq is 50.92, Transformer is 50.01, and finetuned Transformer is 50.19). We also try to relate the results with the language characteristics, distances, family trees, domains, data sizes, and sentence lengths. We find that the effect of the domain is the most important factor affecting the results for the given data domains and sizes. We compare our results with the only existing MT system for Assamese (Bing Translator) and also with pairs involving Hindi.

References

[1]

P. J. Antony. 2013. Machine translation approaches and survey for Indian languages. In Proceedings of the International Journal of Computational Linguistics & Chinese Language Processing, Vol. 18.

[2]

Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio. 2014. Neural machine translation by jointly learning to align and translate. In Proceedings of the 3rd International Conference on Learning Representations (ICLR’15), San Diego, CA, USA, May 7-9, 2015, Yoshua Bengio and Yann LeCun (Eds.). http://arxiv.org/abs/1409.0473.

[3]

Paul Baker, Andrew Hardie, Tony McEnery, Hamish Cunningham, and Robert J. Gaizauskas. 2002. EMILLE, A 67-Million word corpus of Indic languages: Data collection, mark-up and harmonisation. In Proceedings of the 3rd International Conference on Language Resources and Evaluation.

[4]

Satanjeev Banerjee and Alon Lavie. 2005. METEOR: An automatic metric for MT evaluation with improved correlation with human judgments. In Proceedings of the ACL Workshop on Intrinsic and Extrinsic Evaluation Measures for Machine Translation and/or Summarization. 65–72.

[5]

Debajyoty Banik, Asif Ekbal, Pushpak Bhattacharyya, and Siddhartha Bhattacharyya. 2019. Assembling translations from multi-engine machine translation outputs. Applied Soft Computing 78 (2019), 230–239. DOI:https://doi.org/10.1016/j.asoc.2019.02.031

[6]

Kalyanee Kanchan Baruah, Pranjal Das, Abdul Hannan, and Shikhar Kr Sarma. 2014. Assamese-English Bilingual Machine Translation. CoRR abs/1407.2019. http://arxiv.org/abs/1407.2019.

[7]

Luisa Bentivogli, Arianna Bisazza, Mauro Cettolo, and Marcello Federico. 2016. Neural versus Phrase-Based Machine Translation Quality: a Case Study. In Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing. 257–267.

[8]

Piotr Bojanowski, Edouard Grave, Armand Joulin, and Tomas Mikolov. 2017. Enriching word vectors with subword information. Transactions of the Association for Computational Linguistics 5, 1 (2017), 135–146.

[9]

Denny Britz, Anna Goldie, Minh-Thang Luong, and Quoc Le. 2017. Massive Exploration of Neural Machine Translation Architectures. In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing. 1442–1451.

[10]

Peter F. Brown, Stephen A. Della Pietra, Vincent J. Della Pietra, and Robert L. Mercer. 1993. The Mathematics of Statistical Machine Translation: Parameter Estimation. Computational Linguistics 19, 2 (1993), 263–311. Retrieved from https://www.aclweb.org/anthology/J93-2003.

[11]

Sanjay Chatterji, Devshri Roy, Sudeshna Sarkar, and Anupam Basu. 2009. A hybrid approach for bengali to hindi machine translation. In Proceedings of the ICON-2009 7th International Conference on Natural Language Processing. 81–91.

[12]

Kyunghyun Cho, Bart van Merriënboer, Dzmitry Bahdanau, and Yoshua Bengio. 2014. On the properties of neural machine translation: Encoder–decoder approaches. In Proceedings of the SSST-8, 8th Workshop on Syntax, Semantics and Structure in Statistical Translation. 103–111.

[13]

Kyunghyun Cho, Bart Van Merriënboer, Caglar Gulcehre, Dzmitry Bahdanau, Fethi Bougares, Holger Schwenk, and Yoshua Bengio. 2014. Learning phrase representations using RNN encoder-decoder for statistical machine translation. In Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP’14). 1724–1734.

[14]

Raj Dabre, Chenhui Chu, and Anoop Kunchukuttan. 2020. A survey of multilingual neural machine translation. ACM Computing Surveys 53, 5 (2020), 1–38.

[15]

Shaveta Dargan and Munish Kumar. 2019. Writer identification system for indic and non-indic scripts: State-of-the-art survey. Archives of Computational Methods in Engineering 26, 4 (2019), 1283–1311.

[16]

Ayan Das, Pranay Yerra, Ken Kumar, and Sudeshna Sarkar. 2016. A study of attention-based neural machine translation model on Indian languages. In Proceedings of the 6th Workshop on South and Southeast Asian Natural Language Processing. 163–172.

[17]

Pranjal Das and Kalyanee K. Baruah. 2014. Assamese to English statistical machine translation integrated with a transliteration module. International Journal of Computer Applications 100, 5 (2014), 20–24.

[18]

Debajit Deb. 2012. On case marking in assamese bengali and oriya. International Journal of Applied Linguistics & English Literature 1, 2 (2012), 102.

[19]

George Doddington. 2002. Automatic evaluation of machine translation quality using n-gram co-occurrence statistics. In Proceedings of the 2nd International Conference on Human Language Technology Research. 138–145.

[20]

Nadir Durrani, Hassan Sajjad, Alexander Fraser, and Helmut Schmid. 2010. Hindi-to-Urdu machine translation through transliteration. In Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics. Association for Computational Linguistics, 465–474. Retrieved from https://www.aclweb.org/anthology/P10-1048.

[21]

G. V. Garje and G. K. Kharate. 2013. Survey of machine translation systems in India. International Journal on Natural Language Computing 2, 4 (2013), 47–65.

[22]

Jonas Gehring, Michael Auli, David Grangier, Denis Yarats, and Yann N. Dauphin. 2017. Convolutional sequence to sequence learning. In Proceedings of the 34th International Conference on Machine Learning,Vol. 70. JMLR. org, 1243–1252.

[23]

Vikrant Goyal, Sourav Kumar, and Dipti Misra Sharma. 2020. Efficient neural machine translation for low-resource languages via exploiting related languages. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics. 162–168.

[24]

Vishal Goyal and Gurpreet Singh Lehal. 2008. Comparative study of Hindi and Punjabi language scripts. Nepalese Linguistics 23 (2008), 67–82.

[25]

Vishal Goyal and Gurpreet Singh Lehal. 2011. Hindi to Punjabi machine translation system. In Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies: Systems Demonstrations. Association for Computational Linguistics, 1–6.

[26]

Edouard Grave, Piotr Bojanowski, Prakhar Gupta, Armand Joulin, and Tomas Mikolov. 2018. Learning word vectors for 157 languages. In Proceedings of the International Conference on Language Resources and Evaluation.

[27]

Francisco Guzmán, Peng-Jen Chen, Myle Ott, Juan Pino, Guillaume Lample, Philipp Koehn, Vishrav Chaudhary, and Marc’Aurelio Ranzato. 2019. The FLORES evaluation datasets for low-resource machine translation: Nepali–English and Sinhala–English. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing. 6100–6113.

[28]

Md Arid Hasan, Firoj Alam, Shammur Absar Chowdhury, and Naira Khan. 2019. Neural machine translation for the Bangla-English language pair. In Proceedings of the 2019 22nd International Conference on Computer and Information Technology. IEEE, 1–6.

[29]

Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. 2016. Deep residual learning for image recognition. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.

[30]

Wei He, Zhongjun He, Hua Wu, and Haifeng Wang. 2016. Improved neural machine translation with SMT features. In Proceedings of the 13th AAAI Conference on Artificial Intelligence.

[31]

Kenneth Heafield. 2011. KenLM: Faster and smaller language model queries. In Proceedings of the 6th Workshop on Statistical Machine Translation. Association for Computational Linguistics, 187–197.

[32]

Kenneth Heafield, Ivan Pouzyrevsky, Jonathan H. Clark, and Philipp Koehn. 2013. Scalable modified Kneser-Ney language model estimation. In Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics, Vol. 2. 690–696.

[33]

Hieu Hoang and Philipp Koehn. 2008. Design of the moses decoder for statistical machine translation. In Proceedings of the Software Engineering, Testing, and Quality Assurance for Natural Language Processing. Association for Computational Linguistics, 58–65. Retrieved from https://www.aclweb.org/anthology/W08-0510.

[34]

Sepp Hochreiter and Jürgen Schmidhuber. 1997. Long short-term memory. Neural computation 9, 8 (1997), 1735–1780.

[35]

Tanvira Ismail and L. Joyprakash Singh. 2017. Dialect identification of assamese language using spectral features. Indian Journal of Science and Technology 10, 20 (2017), 1–7.

[36]

Hideki Isozaki, Tsutomu Hirao, Kevin Duh, Katsuhito Sudoh, and Hajime Tsukada. 2010. Automatic evaluation of translation quality for distant language pairs. In Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics, 944–952.

[37]

Bushra Jawaid and Daniel Zeman. 2011. Word-order issues in english-to-urdu statistical machine translation. The Prague Bulletin of Mathematical Linguistics 95, 1 (2011), 87–106.

[38]

Sébastien Jean, Kyunghyun Cho, Roland Memisevic, and Yoshua Bengio. 2015. On using very large target vocabulary for neural machine translation. In Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing Vol. 1. Association for Computational Linguistics, 1–10. DOI:https://doi.org/10.3115/v1/P15-1001

[39]

Melvin Johnson, Mike Schuster, Quoc Le, Maxim Krikun, Yonghui Wu, Zhifeng Chen, Nikhil Thorat, Fernanda Viégas, Martin Wattenberg, Greg Corrado, Macduff Hughes, and Jeffrey Dean. 2017. Google’s multilingual neural machine translation system: Enabling zero-shot translation. Transactions of the Association for Computational Linguistics 5 (2017), 339–351. DOI:https://doi.org/10.1162/tacl_a_00065

[40]

Gurpreet Singh Josan and Gurpreet Singh Lehal. 2008. A Punjabi to Hindi machine translation system. In Proceedings of the 22nd International Conference on on Computational Linguistics. Association for Computational Linguistics, 157–160.

[41]

Banikanta Kakati. 1953. Aspects of Early Assamese Literature-1953. Gauhati University.

[42]

Banikanta Kakati and Golockchandra Goswami. 1962. Assamese, its Formation and Development: a Scientific Treatise on the History and Philology of the Assamese Language. Lawyer’s Book Stall.

[43]

Nal Kalchbrenner and Phil Blunsom. 2013. Recurrent continuous translation models. In Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics, 1700–1709. Retrieved from https://www.aclweb.org/anthology/D13-1176.

[44]

Nayan Jyoti Kalita and Baharul Islam. 2015. Bengali to assamese statistical machine translation using moses (corpus based). CoRR abs/1504.01182. http://arxiv.org/abs/1504.01182.

[45]

Harmandeep Kaur and Munish Kumar. 2018. A comprehensive survey on word recognition for non-Indic and Indic scripts. Pattern Analysis and Applications 21, 4 (2018), 897–929.

[46]

Nadeem Jadoon Khan, Waqas Anwar, and Nadir Durrani. 2017. Machine translation approaches and survey for indian languages. CoRR abs/1701.04290. http://arxiv.org/abs/1701.04290.

[47]

Diederik P. Kingma and Jimmy Ba. 2014. Adam: A method for stochastic optimization. In Proceedings of the 3rd International Conference on Learning Representations (ICLR’15), San Diego, CA, USA, May 7-9, 2015. http://arxiv.org/abs/1412.6980.

[48]

Guillaume Klein, Yoon Kim, Yuntian Deng, Jean Senellart, and Alexander M. Rush. 2017. Opennmt: Open-source toolkit for neural machine translation. In Proceedings of ACL 2017, System Demonstrations. 67–72.

[49]

Philipp Koehn. 2005. Europarl: A parallel corpus for statistical machine translation. In Proceedings of the MT Summit, Vol. 5. Citeseer, 79–86.

[50]

Philipp Koehn, Hieu Hoang, Alexandra Birch, Chris Callison-Burch, Marcello Federico, Nicola Bertoldi, Brooke Cowan, Wade Shen, Christine Moran, Richard Zens, Chris Dyer, Ondej Bojar, Alex Constantin, and Evan Herbst. 2007. Moses: Open source toolkit for statistical machine translation. In Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics Companion Volume Proceedings of the Demo and Poster Sessions. 177–180.

[51]

Philipp Koehn and Rebecca Knowles. 2017. Six challenges for neural machine translation. In Proceedings of the 1st Workshop on Neural Machine Translation. 28–39.

[52]

Philipp Koehn, Franz Josef Och, and Daniel Marcu. 2003. Statistical phrase-based translation. In Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology, Vol. 1. Association for Computational Linguistics, 48–54.

[53]

Munish Kumar, M. K. Jindal, and R. K. Sharma. 2011. Review on OCR for handwritten Indian scripts character recognition. In Proceedings of the International Conference on Digital Image Processing and Information Technology. Springer, 268–276.

[54]

Munish Kumar, M. K. Jindal, and R. K. Sharma. 2016. A novel framework for grading of writers using offline Gurmukhi characters. Proceedings of the National Academy of Sciences, India Section A: Physical Sciences 86, 3 (2016), 405–415.

[55]

Munish Kumar, Manish Kumar Jindal, Rajendra Kumar Sharma, and Simpel Rani Jindal. 2019. Character and numeral recognition for non-Indic and Indic scripts: A survey. Artificial Intelligence Review 52, 4 (2019), 2235–2261.

[56]

Munish Kumar, Manish Kumar Jindal, Rajendra Kumar Sharma, and Simpel Rani Jindal. 2020. Performance evaluation of classifiers for the recognition of offline handwritten Gurmukhi characters and numerals: A study. Artificial Intelligence Review 53, 3 (2020), 2075–2097.

[57]

Munish Kumar, Simpel Rani Jindal, Manish Kumar Jindal, and Gurpreet Singh Lehal. 2019. Improved recognition results of medieval handwritten Gurmukhi manuscripts using boosting and bagging methodologies. Neural Processing Letters 50, 1 (2019), 43–56.

[58]

Bornini Lahiri. 2018. Classifiers in surjapuri. Jadavpur Journal of Languages and Linguistics 2, 1 (2018), 27–37.

[59]

Sahinur Rahman Laskar, Abdullah Faiz Ur Rahman Khilji, Partha Pakray, and Sivaji Bandyopadhyay. 2020. EnAsCorp1. 0: English-Assamese Corpus. In Proceedings of the 3rd Workshop on Technologies for MT of Low Resource Languages. 62–68.

[60]

Alon Lavie. 2010. Evaluating the output of machine translation systems. In Proceedings of the 9th Conference of the Association for Machine Translation in the Americas: Tutorials. Association for Machine Translation in the Americas. https://aclanthology.org/2010.amta-tutorials.4.

[61]

Gregor Leusch, Nicola Ueffing, and Hermann Ney. 2006. CDER: Efficient MT evaluation using block movements. In Proceedings of the 11th Conference of the European Chapter of the Association for Computational Linguistics.

[62]

Vladimir I. Levenshtein. 1966. Binary codes capable of correcting deletions, insertions, and reversals. Soviet Physics Doklady 10, 8 (1966), 707–710.

[63]

Wang Ling, Tiago Luís, Luís Marujo, Ramón Fernandez Astudillo, Silvio Amir, Chris Dyer, Alan W. Black, and Isabel Trancoso. 2015. Finding function in form: Compositional character models for open vocabulary word representation. In Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing. 1520–1530.

[64]

Wang Ling, Yulia Tsvetkov, Silvio Amir, Ramon Fermandez, Chris Dyer, Alan W. Black, Isabel Trancoso, and Chu-Cheng Lin. 2015. Not all contexts are created equal: Better word representations with variable attention. In Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing. 1367–1372.

[65]

Minh-Thang Luong, Hieu Pham, and Christopher D. Manning. 2015. Effective approaches to attention-based neural machine translation. In Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing. 412–1421.

[66]

Colin P. Masica. 1991. The Indo-Aryan Languages. Cambridge University Press, Cambridge.

[67]

Colin P. Masica. 2005. A new survey of the Indo-Aryan languages. The Journal of the American Oriental Society 125, 1 (2005), 79–90.

[68]

Mohammad Abdullah Al Mumin, Md Hanif Seddiqui, Muhammed Zafar Iqbal, and Mohammed Jahirul Islam. 2019. Neural machine translation for low-resource English-Bangla. Journal of Computer Science 15, 11 (2019), 1627–1637. DOI:https://doi.org/10.3844/jcssp.2019.1627.1637

[69]

Rajesh Kumar Mundotiya, Manish Kumar Singh, Rahul Kapur, Swasti Mishra, and Anil Kumar Singh. 2021. Basic linguistic resources and baselines for Bhojpuri, Magahi and Maithili for natural language processing. ACM Trans. Asian Low-Resour. Lang. Inf. Process. 20, 6, Article 95 (2021), 37 pages.

[70]

Franz Josef Och. 2003. Minimum error rate training in statistical machine translation. In Proceedings of the 41st Annual Meeting on Association for Computational Linguistics, Vol. 1. Association for Computational Linguistics, 160–167.

[71]

Franz Josef Och and Hermann Ney. 2003. A systematic comparison of various statistical alignment models. Computational linguistics 29, 1 (2003), 19–51.

[72]

Atul Kr Ojha, Ritesh Kumar, Akanksha Bansal, and Priya Rani. 2019. Panlingua-KMI MT system for similar language translation task at WMT 2019. In Proceedings of the 4th Conference on Machine Translation, Vol. 3. 213–218.

[73]

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 on Association for Computational Linguistics. Association for Computational Linguistics, 311–318.

[74]

D. P. PATTANAYAK. 2016. ORIYA and ASSAMESE. Current Trends in Linguistics.De Gruyter Mouton, 122–152.

[75]

Jerin Philip, Vinay P. Namboodiri, and C. V. Jawahar. 2019. A baseline neural machine translation system for Indian languages. CoRR abs/1907.12437 (2019). https://dblp.org/rec/journals/corr/abs-1907-12437.bib.

[76]

Ananthakrishnan Ramanathan, Jayprasad Hegde, Ritesh M. Shah, Pushpak Bhattacharyya, and Sasikumar M.2008. Simple syntactic and morphological processing can help English-Hindi statistical machine translation. In Proceedings of the 3rd International Joint Conference on Natural Language Processing, Vol. 1. Retrieved from https://www.aclweb.org/anthology/I08-1067.

[77]

Shuo Ren, Zhirui Zhang, Shujie Liu, Ming Zhou, and Shuai Ma. 2019. Unsupervised neural machine translation with smt as posterior regularization. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 33. 241–248.

[78]

Karthik Revanuru, Kaushik Turlapaty, and Shrisha Rao. 2017. Neural machine translation of Indian languages. In Proceedings of the 10th Annual ACM India Compute Conference. ACM, 11–20.

[79]

Navanath Saharia, Kishori M. Konwar, Utpal Sharma, and Jugal K. Kalita. 2013. An improved stemming approach using HMM for a highly inflectional language. In Proceedings of the International Conference on Intelligent Text Processing and Computational Linguistics. Springer, 164–173.

[80]

Sukanta Sen, Kamal Kumar Gupta, Asif Ekbal, and Pushpak Bhattacharyya. 2018. IITP-MT at WAT2018: Transformer-based multilingual Indic-English neural machine translation system. In Proceedings of the 32nd Pacific Asia Conference on Language, Information and Computation: 5th Workshop on Asian Translation.

[81]

Debapriya Sengupta and Goutam Saha. 2015. Study on similarity among Indian languages using language verification framework. Advances in Artificial Intelligence 2015, Article 2 (2015), 1.

[82]

Rico Sennrich, Barry Haddow, and Alexandra Birch. 2016. Improving Neural Machine Translation Models with Monolingual Data. In Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics, Vol. 1. 86–96.

[83]

Rico Sennrich, Barry Haddow, and Alexandra Birch. 2016. Neural machine translation of rare words with Subword units. In Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics, Vol. 1. 1715–1725.

[84]

Parth Shah and Vishvajit Bakrola. 2019. Neural machine translation system of Indic languages-an attention based approach. In Proceedings of the 2019 2nd International Conference on Advanced Computational and Communication Paradigms. IEEE, 1–5.

[85]

Amitoj Singh, Virender Kadyan, Munish Kumar, and Nancy Bassan. 2020. ASRoIL: A comprehensive survey for automatic speech recognition of Indian languages. Artificial Intelligence Review 53, 5 (2020), 3673–3704.

[86]

Anil Kumar Singh. 2010. Modeling and Application of Linguistic Similarity. Ph.D. Dissertation. International Institute of Information Technology, Hyderabad, India.

[87]

Muskaan Singh, Ravinder Kumar, and Inderveer Chana. 2019. Neural-based machine translation system outperforming statistical phrase-based machine translation for low-resource languages. In Proceedings of the 2019 12th International Conference on Contemporary Computing. IEEE, 1–7.

[88]

Moirangthem Tiken Singh, Rajdeep Borgohain, and Sourav Gohain. 2014. An English-assamese machine translation system. International Journal of Computer Applications 93, 4 (2014), 1–6.

[89]

R. Mahesh and K. Sinha. 2004. An engineering perspective of machine translation: anglabharti-II and anubharti-II architectures. In Proceedings of the International Symposium on Machine Translation, NLP and Translation Support System. 10–17.

[90]

Matthew Snover, Bonnie Dorr, Richard Schwartz, Linnea Micciulla, and John Makhoul. 2006. A study of translation edit rate with targeted human annotation. In Proceedings of the Association for Machine Translation in the Americas, Vol. 200.

[91]

Nitish Srivastava, Geoffrey Hinton, Alex Krizhevsky, Ilya Sutskever, and Ruslan Salakhutdinov. 2014. Dropout: A simple way to prevent neural networks from overfitting. Journal of Machine Learning Research 15, 1 (2014), 1929–1958. Retrieved from http://jmlr.org/papers/v15/srivastava14a.html.

[92]

I. Sutskever, O. Vinyals, and Q. V. Le. 2014. Sequence to sequence learning with neural networks. In Proceedings of the Advances in Neural Information Processing Systems.

[93]

Christoph Tillmann, Stephan Vogel, Hermann Ney, Arkaitz Zubiaga, and Hassan Sawaf. 1997. Accelerated DP based search for statistical translation. In Proceedings of the 5th European Conference on Speech Communication and Technology.

[94]

Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Łukasz Kaiser, and Illia Polosukhin. 2017. Attention is all you need. In Proceedings of the Advances in Neural Information Processing Systems. 5998–6008.

[95]

Xing Wang, Zhengdong Lu, Zhaopeng Tu, Hang Li, Deyi Xiong, and Min Zhang. 2017. Neural machine translation advised by statistical machine translation. In Proceedings of the 31st AAAI Conference on Artificial Intelligence.

[96]

Yining Wang, Jiajun Zhang, Feifei Zhai, Jingfang Xu, and Chengqing Zong. 2018. Three strategies to improve one-to-many multilingual translation. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing. 2955–2960.

[97]

Barret Zoph and Kevin Knight. 2016. Multi-source neural translation. In Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. 30–34.

[98]

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. 1568–1575.

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DOI:10.1145/3494068

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Published: 16 November 2021

Accepted: 01 June 2021

Revised: 01 May 2021

Received: 01 July 2020

Published in TALLIP Volume 21, Issue 1

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Affiliations

Rupjyoti Baruah

Indian Institute Technology, BHU, Varanasi, Uttar-pradesh, INDIA

Rajesh Kumar Mundotiya

Indian Institute Technology, BHU, Varanasi, Uttar-pradesh, INDIA

Anil Kumar Singh

Indian Institute Technology, BHU, Varanasi, Uttar-pradesh, INDIA

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