Leveraging Neural Machine Translation for Word Alignment

Vilém Zouhar, Daria Pylypenko

References:

  1. Tamer Alkhouli, Gabriel Bretschner, Jan-Thorsten Peter, Mohammed Hethnawi, Andreas Guta, and Hermann Ney. Alignment-based neural machine translation In Proceedings of the First Conference on Machine Translation: Volume 1, Research Papers, pages 54–65, 2016. (http://doi.org/10.18653/v1/W16-2206)
  2. Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio. Neural machine translation by jointly learning to align and translate arXiv preprint arXiv:1409.0473, 2014.
  3. Ergun Biçici. The Regression Model of Machine Translation, Supervisor: Deniz Yuret, 2011.
  4. Wenhu Chen, Evgeny Matusov, Shahram Khadivi, and Jan-Thorsten Peter. Guided alignment training for topic-aware neural machine translation arXiv preprint arXiv:1607.01628, 2016.
  5. Yun Chen, Yang Liu, Guanhua Chen, Xin Jiang, and Qun Liu. Accurate Word Alignment Induction from Neural Machine Translation arXiv preprint arXiv:2004.14837, 2020.
  6. Chi Chen, Maosong Sun, and Yang Liu. Mask-Align: Self-Supervised Neural Word Alignment arXiv preprint arXiv:2012.07162, 2020.
  7. Tom Kocmi, Martin Popel, and Ondřej Bojar. Announcing CzEng 2.0 parallel corpus with over 2 gigawords arXiv preprint arXiv:2007.03006, 2020.
  8. Chris Dyer, Victor Chahuneau, and Noah A Smith. A simple, fast, and effective reparameterization of ibm model 2 In Proceedings of the 2013 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 644–648, 2013.
  9. Marcin Junczys-Dowmunt and Arkadiusz Szał. Symgiza++: symmetrized word alignment models for statistical machine translation In International Joint Conferences on Security and Intelligent Information Systems, pages 379–390, 2011. (http://doi.org/10.1007/978-3-642-25261-7_30)
  10. Marcin Junczys-Dowmunt, Roman Grundkiewicz, Tomasz Dwojak, Hieu Hoang, Kenneth Heafield, Tom Neckermann, Frank Seide, Ulrich Germann, Alham Fikri Aji, Nikolay Bogoychev, and others. Marian: Fast neural machine translation in C++ arXiv preprint arXiv:1804.00344, 2018. (http://doi.org/10.18653/v1/P18-4020)
  11. Marcin Junczys-Dowmunt, Kenneth Heafield, Hieu Hoang, Roman Grundkiewicz, and Anthony Aue. Marian: Cost-effective high-quality neural machine translation in C++ arXiv preprint arXiv:1805.12096, 2018. (http://doi.org/10.18653/v1/W18-2716)
  12. Philipp Koehn. Statistical machine translation, Cambridge University Press, 2009. (http://doi.org/10.1017/CBO9780511815829)
  13. Taku Kudo and John Richardson. Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing arXiv preprint arXiv:1808.06226, 2018. (http://doi.org/10.18653/v1/D18-2012)
  14. Xintong Li, Guanlin Li, Lemao Liu, Max Meng, and Shuming Shi. On the word alignment from neural machine translation In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 1293–1303, 2019. (http://doi.org/10.18653/v1/P19-1124)
  15. Percy Liang, Ben Taskar, and Dan Klein. Alignment by agreement In Proceedings of the Human Language Technology Conference of the NAACL, Main Conference, pages 104–111, 2006. (http://doi.org/10.3115/1220835.1220849)
  16. David Mareček. Czech-English Manual Word Alignment, {LINDAT}/{CLARIAH}-{CZ} digital library at the Institute of Formal and Applied Linguistics ({{Ú}FAL}), Faculty of Mathematics and Physics, Charles University, 2016.
  17. Rada Mihalcea and Ted Pedersen. An evaluation exercise for word alignment In Proceedings of the HLT-NAACL 2003 Workshop on Building and using parallel texts: data driven machine translation and beyond, pages 1–10, 2003. (http://doi.org/10.3115/1118905.1118906)
  18. Ulrich Germann, Roman Grundkiewicz, Martin Popel, Radina Dobreva, Nikolay Bogoychev, and Kenneth Heafield. Speed-optimized, Compact Student Models that Distill Knowledge from a Larger Teacher Model: the UEDIN-CUNI Submission to the WMT 2020 News Translation Task In Proceedings of the Fifth Conference on Machine Translation, pages 190–195, Association for Computational Linguistics, Online, 2020.
  19. Nikolay Bogoychev, Roman Grundkiewicz, Alham Fikri Aji, Maximiliana Behnke, Kenneth Heafield, Sidharth Kashyap, Emmanouil-Ioannis Farsarakis, and Mateusz Chudyk. Edinburgh's Submissions to the 2020 Machine Translation Efficiency Task In Proceedings of the Fourth Workshop on Neural Generation and Translation, pages 218–224, Association for Computational Linguistics, Online, 2020.
  20. Franz Josef Och and Hermann Ney. Improved statistical alignment models In Proceedings of the 38th annual meeting of the association for computational linguistics, pages 440–447, 2000. (http://doi.org/10.3115/1075218.1075274)
  21. Franz Josef Och and Hermann Ney. A systematic comparison of various statistical alignment models Computational linguistics 29, pages 19–51, MIT Press, 2003. (http://doi.org/10.1162/089120103321337421)
  22. Roberts Rozis and Raivis Skadiņš. Tilde MODEL-multilingual open data for EU languages In Proceedings of the 21st Nordic Conference on Computational Linguistics, pages 263–265, 2017.
  23. Masoud Jalili Sabet, Philipp Dufter, and Hinrich Schütze. Simalign: High quality word alignments without parallel training data using static and contextualized embeddings arXiv preprint arXiv:2004.08728, 2020.
  24. Matt Post. A Call for Clarity in Reporting BLEU Scores In Proceedings of the Third Conference on Machine Translation: Research Papers, pages 186–191, Association for Computational Linguistics, Belgium, Brussels, 2018. (http://doi.org/10.18653/v1/W18-6319)
  25. Bettina Schrader. How does morphological complexity translate? A cross-linguistic case study for word alignment In Proceedings of Linguistic Evidence Conference, pages 189–191, 2006.
  26. Lucia Specia, Kashif Shah, José GC De Souza, and Trevor Cohn. QuEst-A translation quality estimation framework In Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pages 79–84, 2013.
  27. Evgeny Matusov, Richard Zens, and Hermann Ney. Symmetric word alignments for statistical machine translation, pages , 2004. (http://doi.org/10.3115/1220355.1220387)
  28. Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser, and Illia Polosukhin. Attention Is All You Need, 2017.
  29. Di Wu, Liang Ding, Shuo Yang, and Dacheng Tao. SLUA: A Super Lightweight Unsupervised Word Alignment Model via Cross-Lingual Contrastive Learning arXiv preprint arXiv:2102.04009, 2021.
  30. Thomas Zenkel, Joern Wuebker, and John DeNero. Adding interpretable attention to neural translation models improves word alignment arXiv preprint arXiv:1901.11359, 2019.
  31. Luisa M Zintgraf, Taco S Cohen, Tameem Adel, and Max Welling. Visualizing deep neural network decisions: Prediction difference analysis arXiv preprint arXiv:1702.04595, 2017.
  32. Vilém Zouhar and Michal Novák. Extending Ptakopět for Machine Translation User Interaction Experiments The Prague Bulletin of Mathematical Linguistics 115, pages 129–142, 2020. (http://doi.org/10.14712/00326585.008)