機器翻譯,又稱為自動翻譯,是利用計算機將一種自然語言(源語言)轉換為另一種自然語言(目標語言)的過程。它是計算語言學的一個分支,是人工智能的終極目標之一,具有重要的科學研究價值。

知識薈萃

機器翻譯 Machine Translation 專知薈萃

入門學習

綜述

進階論文

1997

  1. Neco, R. P., & Forcada, M. L. (1997, June). Asynchronous translations with recurrent neural nets. In Neural Networks, 1997., International Conference on (Vol. 4, pp. 2535-2540). IEEE.
    [http://ieeexplore.ieee.org/document/614693/]

2003

  1. Bengio, Y., Ducharme, R., Vincent, P., & Jauvin, C. (2003). A neural probabilistic language model. Journal of machine learning research, 3(Feb), 1137-1155.
    [http://www.jmlr.org/papers/volume3/bengio03a/bengio03a.pdf]
  2. Pascanu, R., Mikolov, T., & Bengio, Y. (2013, February). On the difficulty of training recurrent neural networks. In International Conference on Machine Learning (pp. 1310-1318).
    [http://arxiv.org/abs/1211.5063]

2010

  1. Sudoh, K., Duh, K., Tsukada, H., Hirao, T., & Nagata, M. (2010, July). Divide and translate: improving long distance reordering in statistical machine translation. In Proceedings of the Joint Fifth Workshop on Statistical Machine Translation and MetricsMATR (pp. 418-427). Association for Computational Linguistics.
    [https://dl.acm.org/citation.cfm?id=1868912]

2013

  1. Kalchbrenner, N., & Blunsom, P. (2013, October). Recurrent Continuous Translation Models. In EMNLP (Vol. 3, No. 39, p. 413).
    [https://www.researchgate.net/publication/289758666_Recurrent_continuous_translation_models]

2014

  1. Mnih, V., Heess, N., & Graves, A. (2014). Recurrent models of visual attention. In Advances in neural information processing systems (pp. 2204-2212)
    [http://arxiv.org/abs/1406.6247]
  2. Sutskever, I., Vinyals, O., & Le, Q. V. Sequence to sequence learning with neural networks. In Advances in neural information processing systems(pp. 3104-3112).
    [https://arxiv.org/abs/1409.3215]
  3. Cho, K., Van Merriënboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., & Bengio, Y. . Learning phrase representations using RNN encoder-decoder for statistical machine translation. arXiv preprint arXiv:1406.1078.
    [http://arxiv.org/abs/1406.1078]
  4. Bahdanau, D., Cho, K., & Bengio, Y. (2014). Neural machine translation by jointly learning to align and translate. arXiv preprint arXiv:1409.0473.
    [https://arxiv.org/abs/1409.0473]
  5. Jean, S., Cho, K., Memisevic, R., & Bengio, Y. (2014). On using very large target vocabulary for neural machine translation. arXiv preprint arXiv:1412.2007.
    [http://arxiv.org/abs/1412.2007]
  6. Luong, M. T., Sutskever, I., Le, Q. V., Vinyals, O., & Zaremba, W. (2014). Addressing the rare word problem in neural machine translation. arXiv preprint arXiv:1410.8206.
    [http://arxiv.org/abs/1410.8206]

2015

  1. Sennrich, R., Haddow, B., & Birch, A. (2015). Improving neural machine translation models with monolingual data. arXiv preprint arXiv:1511.06709.
    [http://arxiv.org/abs/1511.06709]
  2. Dong, D., Wu, H., He, W., Yu, D., & Wang, H. (2015). Multi-Task Learning for Multiple Language Translation. In ACL (1) (pp. 1723-1732).
    [http://www.anthology.aclweb.org/P/P15/P15-1166.pdf]
  3. Shen, S., Cheng, Y., He, Z., He, W., Wu, H., Sun, M., & Liu, Y. (2015). Minimum risk training for neural machine translation. arXiv preprint arXiv:1512.02433.
    [https://arxiv.org/abs/1512.02433]
  4. Bojar O, Chatterjee R, Federmann C, et al. Findings of the 2015 Workshop on Statistical Machine Translation[C]. Tech Workshop on Statistical Machine Translation,2015.
    [https://www-test.pure.ed.ac.uk/portal/files/23139669/W15_3001.pdfv]

2016

  1. Facebook:Convolutional Sequence to Sequence Learning Jonas Gehring, Michael Auli, David Grangier, Denis Yarats, Yann N. Dauphin
    [https://arxiv.org/abs/1705.03122]
  2. Wu, Y., Schuster, M., Chen, Z., Le, Q. V., Norouzi, M., Macherey, W., … & Klingner, J. (2016). Google's neural machine translation system: Bridging the gap between human and machine translation. arXiv preprint arXiv:1609.08144.
    [https://arxiv.org/abs/1609.08144v1]
  3. Gehring, J., Auli, M., Grangier, D., & Dauphin, Y. N. (2016). A convolutional encoder model for neural machine translation. arXiv preprint arXiv:1611.02344.
    [https://arxiv.org/abs/1611.02344]
  4. Cheng, Y., Xu, W., He, Z., He, W., Wu, H., Sun, M., & Liu, Y. (2016). Semi-supervised learning for neural machine translation. arXiv preprint arXiv:1606.04596.
    [http://arxiv.org/abs/1606.04596]
  5. Wang, M., Lu, Z., Li, H., & Liu, Q. (2016). Memory-enhanced decoder for neural machine translation. arXiv preprint arXiv:1606.02003.
    [https://arxiv.org/abs/1606.02003]
  6. Sennrich, R., & Haddow, B. (2016). Linguistic input features improve neural machine translation. arXiv preprint arXiv:1606.02892.
    [http://arxiv.org/abs/1606.02892]
  7. Tu, Z., Lu, Z., Liu, Y., Liu, X., & Li, H. (2016). Modeling coverage for neural machine translation. arXiv preprint arXiv:1601.04811.
    [http://arxiv.org/abs/1601.04811]
  8. Cohn, T., Hoang, C. D. V., Vymolova, E., Yao, K., Dyer, C., & Haffari, G. (2016). Incorporating structural alignment biases into an attentional neural translation model. arXiv preprint arXiv:1601.01085.
    [http://www.m-mitchell.com/NAACL-2016/NAACL-HLT2016/pdf/N16-1102.pdf]
  9. Hitschler, J., Schamoni, S., & Riezler, S. (2016). Multimodal pivots for image caption translation. arXiv preprint arXiv:1601.03916.
    [https://arxiv.org/abs/1601.03916]
  10. Junczys-Dowmunt, M., Dwojak, T., & Hoang, H. (2016). Is neural machine translation ready for deployment. A case study on, 30.
    [https://arxiv.org/abs/1610.01108]
  11. Johnson, M., Schuster, M., Le, Q. V., Krikun, M., Wu, Y., Chen, Z., … & Hughes, M. (2016). Google』s multilingual neural machine translation system: enabling zero-shot translation. arXiv preprint arXiv:1611.04558.
    [https://arxiv.org/abs/1611.04558]
  12. Bartolome, Diego, and Gema Ramirez.「Beyond the Hype of Neural Machine Translation,」MIT Technology Review (May 23, 2016), bit.ly/2aG4bvR.
    [https://www.slideshare.net/TAUS/beyond-the-hype-of-neural-machine-translation-diego-bartolome-tauyou-and-gema-ramirez-prompsit-language-engineering]
  13. Crego, J., Kim, J., Klein, G., Rebollo, A., Yang, K., Senellart, J., … & Enoue, S. (2016). SYSTRAN』s Pure Neural Machine Translation Systems. arXiv preprint arXiv:1610.05540.
    [https://arxiv.org/abs/1610.05540]

2017

  1. Google:Attention Is All You Need Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser, Illia Polosukhin
    [http://arxiv.org/abs/1706.03762]
  2. Microsoft: Neural Phrase-based Machine Translation Po-Sen Huang, Chong Wang, Dengyong Zhou, Li Deng
    [http://arxiv.org/abs/1706.05565]
  3. A Neural Network for Machine Translation, at Production Scale. (2017). Research Blog. Retrieved 26 July 2017, from [https://research.googleblog.com/2016/09/a-neural-network-for-machine.html]
    [http://www.googblogs.com/a-neural-network-for-machine-translation-at-production-scale/]
  4. Gehring, J., Auli, M., Grangier, D., Yarats, D., & Dauphin, Y. N. (2017). Convolutional Sequence to Sequence Learning. arXiv preprint arXiv:1705.03122.
    [https://arxiv.org/abs/1705.03122]
  5. Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., … & Polosukhin, I. (2017). Attention Is All You Need. arXiv preprint arXiv:1706.03762.
    [https://arxiv.org/abs/1706.03762]
  6. Train Neural Machine Translation Models with Sockeye | Amazon Web Services. (2017). Amazon Web Services. Retrieved 26 July 2017, from
    [https://aws.amazon.com/blogs/ai/train-neural-machine-translation-models-with-sockeye/]
  7. Dandekar, N. (2017). How does an attention mechanism work in deep learning for natural language processing?. Quora. Retrieved 26 July 2017, from
    [https://www.quora.com/How-does-an-attention-mechanism-work-in-deep-learning-for-natural-language-processing]
  8. Microsoft Translator launching Neural Network based translations for all its speech languages. (2017). Translator. Retrieved 27 July 2017, from
    [https://blogs.msdn.microsoft.com/translation/2016/11/15/microsoft-translator-launching-neural-network-based-translations-for-all-its-speech-languages/]
  9. ACL 2017. (2017). Accepted Papers, Demonstrations and TACL Articles for ACL 2017. [online] Available at:
    [https://chairs-blog.acl2017.org/2017/04/05/accepted-papers-and-demonstrations/] [Accessed 7 Aug. 2017].

2018

  1. Miguel Domingo, Álvaro Peris and Francisco Casacuberta. 2018. Segment-based interactive-predictive machine translation. Machine Translation.[https://www.researchgate.net/publication/322275484_Segment-based_interactive-predictive_machine_translation] [Citation: 2]

  2. Xin Wang, Wenhu Chen, Yuan-Fang Wang, and William Yang Wang. 2018. No Metrics Are Perfect: Adversarial Reward Learning for Visual Storytelling. In Proceedings of ACL 2018.[http://aclweb.org/anthology/P18-1083] [Citation: 10]

  3. Arun Tejasvi Chaganty, Stephen Mussman, and Percy Liang. 2018. The price of debiasing automatic metrics in natural language evaluation.[https://arxiv.org/pdf/1807.02202] [In Proceedings of ACL 2018.]

  4. Xin Wang, Wenhu Chen, Yuan-Fang Wang, and William Yang Wang. 2018.No Metrics Are Perfect: Adversarial Reward Learning for Visual Storytelling. In Proceedings of ACL 2018. (Citation: 10)

  5. Arun Tejasvi Chaganty, Stephen Mussman, and Percy Liang. 2018.The price of debiasing automatic metrics in natural language evaluation. In Proceedings of ACL 2018.

  6. Lukasz Kaiser, Aidan N. Gomez, and Francois Chollet. 2018.Depthwise Separable Convolutions for Neural Machine Translation. In Proceedings of ICLR 2018. (Citation: 27)

  7. Yanyao Shen, Xu Tan, Di He, Tao Qin, and Tie-Yan Liu. 2018.Dense Information Flow for Neural Machine Translation. In Proceedings of NAACL 2018. (Citation: 3)

  8. Wenhu Chen, Guanlin Li, Shuo Ren, Shujie Liu, Zhirui Zhang, Mu Li, and Ming Zhou. 2018.Generative Bridging Network for Neural Sequence Prediction. In Proceedings of NAACL 2018. (Citation: 3)

  9. Mia Xu Chen, Orhan Firat, Ankur Bapna, Melvin Johnson, Wolfgang Macherey, George Foster, Llion Jones, Mike Schuster, Noam Shazeer, Niki Parmar, Ashish Vaswani, Jakob Uszkoreit, Lukasz Kaiser, Zhifeng Chen, Yonghui Wu, and Macduff Hughes. 2018.The Best of Both Worlds: Combining Recent Advances in Neural Machine Translation. In Proceedings of ACL 2018. (Citation: 22)

  10. Weiyue Wang, Derui Zhu, Tamer Alkhouli, Zixuan Gan, and Hermann Ney. 2018.Neural Hidden Markov Model for Machine Translation. In Proceedings of ACL 2018. (Citation: 3)

  11. Jingjing Gong, Xipeng Qiu, Shaojing Wang, and Xuanjing Huang. 2018.Information Aggregation via Dynamic Routing for Sequence Encoding. In COLING 2018.

  12. Qiang Wang, Fuxue Li, Tong Xiao, Yanyang Li, Yinqiao Li, and Jingbo Zhu. 2018.Multi-layer Representation Fusion for Neural Machine Translation. In Proceedings of COLING 2018 .

  13. Yachao Li, Junhui Li, and Min Zhang. 2018.Adaptive Weighting for Neural Machine Translation. In Proceedings of COLING 2018 .

  14. Kaitao Song, Xu Tan, Di He, Jianfeng Lu, Tao Qin, and Tie-Yan Liu. 2018.Double Path Networks for Sequence to Sequence Learning. In Proceedings of COLING 2018 .

  15. Zi-Yi Dou, Zhaopeng Tu, Xing Wang, Shuming Shi, and Tong Zhang. 2018.Exploiting Deep Representations for Neural Machine Translation. In Proceedings of EMNLP 2018 . (Citation: 1)

  16. Biao Zhang, Deyi Xiong, Jinsong Su, Qian Lin, and Huiji Zhang. 2018.Simplifying Neural Machine Translation with Addition-Subtraction Twin-Gated Recurrent Networks. In Proceedings of EMNLP 2018 .

  17. Gongbo Tang, Mathias Müller, Annette Rios, and Rico Sennrich. 2018.Why Self-Attention? A Targeted Evaluation of Neural Machine Translation Architectures. In Proceedings of EMNLP 2018 . (Citation: 6)

  18. Ke Tran, Arianna Bisazza, and Christof Monz. 2018.The Importance of Being Recurrent for Modeling Hierarchical Structure. In Proceedings of EMNLP 2018 . (Citation: 6)

  19. Parnia Bahar, Christopher Brix, and Hermann Ney. 2018.Towards Two-Dimensional Sequence to Sequence Model in Neural Machine Translation. In Proceedings of EMNLP 2018 . (Citation: 1)

  20. Tianyu He, Xu Tan, Yingce Xia, Di He, Tao Qin, Zhibo Chen, and Tie-Yan Liu. 2018.Layer-Wise Coordination between Encoder and Decoder for Neural Machine Translation. In Proceedings of NeurIPS 2018 . (Citation: 2)

  21. Harshil Shah and David Barber. 2018.Generative Neural Machine Translation. In Proceedings of NeurIPS 2018 .

  22. Hany Hassan, Anthony Aue, Chang Chen, Vishal Chowdhary, Jonathan Clark, Christian Federmann, Xuedong Huang, Marcin Junczys-Dowmunt, William Lewis, Mu Li, Shujie Liu, Tie-Yan Liu, Renqian Luo, Arul Menezes, Tao Qin, Frank Seide, Xu Tan, Fei Tian, Lijun Wu, Shuangzhi Wu, Yingce Xia, Dongdong Zhang, Zhirui Zhang, and Ming Zhou. 2018.Achieving Human Parity on Automatic Chinese to English News Translation. Technical report. Microsoft AI & Research. (Citation: 41)

  23. Tao Shen, Tianyi Zhou, Guodong Long, Jing Jiang, Shirui Pan, and Chengqi Zhang. 2018.DiSAN: Directional Self-Attention Network for RNN/CNN-Free Language Understanding. In Proceedings of AAAI 2018 . (Citation: 60)

  24. Tao Shen, Tianyi Zhou, Guodong Long, Jing Jiang, and Chengqi Zhang. 2018.Bi-directional Block Self-attention for Fast and Memory-efficient Sequence Modeling. In Proceedings of ICLR 2018 . (Citation: 13)

  25. Tao Shen, Tianyi Zhou, Guodong Long, Jing Jiang, Sen Wang, Chengqi Zhang. 2018.Reinforced Self-Attention Network: a Hybrid of Hard and Soft Attention for Sequence Modeling. In Proceedings of IJCAI 2018 . (Citation: 18)

  26. Peter Shaw, Jakob Uszkorei, and Ashish Vaswani. 2018.Self-Attention with Relative Position Representations. In Proceedings of NAACL 2018 . (Citation: 24)

  27. Lesly Miculicich Werlen, Nikolaos Pappas, Dhananjay Ram, and Andrei Popescu-Belis. 2018.Self-Attentive Residual Decoder for Neural Machine Translation. In Proceedings of NAACL 2018 . (Citation: 3)

  28. Xintong Li, Lemao Liu, Zhaopeng Tu, Shuming Shi, and Max Meng. 2018.Target Foresight Based Attention for Neural Machine Translation. In Proceedings of NAACL 2018 .

  29. Biao Zhang, Deyi Xiong, and Jinsong Su. 2018.Accelerating Neural Transformer via an Average Attention Network. In Proceedings of ACL 2018 . (Citation: 5)

  30. Tobias Domhan. 2018.How Much Attention Do You Need? A Granular Analysis of Neural Machine Translation Architectures. In Proceedings of ACL 2018 . (Citation: 3)

  31. Shaohui Kuang, Junhui Li, António Branco, Weihua Luo, and Deyi Xiong. 2018.Attention Focusing for Neural Machine Translation by Bridging Source and Target Embeddings. In Proceedings of ACL 2018 . (Citation: 1)

  32. Chaitanya Malaviya, Pedro Ferreira, and André F. T. Martins. 2018.Sparse and Constrained Attention for Neural Machine Translation. In Proceedings of ACL 2018 . (Citation: 4)

  33. Jian Li, Zhaopeng Tu, Baosong Yang, Michael R. Lyu, and Tong Zhang. 2018.Multi-Head Attention with Disagreement Regularization. In Proceedings of EMNLP 2018 . (Citation: 1)

  34. Wei Wu, Houfeng Wang, Tianyu Liu and Shuming Ma. 2018.Phrase-level Self-Attention Networks for Universal Sentence Encoding. In Proceedings of EMNLP 2018 .

  35. Baosong Yang, Zhaopeng Tu, Derek F. Wong, Fandong Meng, Lidia S. Chao, and Tong Zhang. 2018.Modeling Localness for Self-Attention Networks. In Proceedings of EMNLP 2018 . (Citation: 2)

  36. Junyang Lin, Xu Sun, Xuancheng Ren, Muyu Li, and Qi Su. 2018.Learning When to Concentrate or Divert Attention: Self-Adaptive Attention Temperature for Neural Machine Translation. In Proceedings of EMNLP 2018 .

  37. Shiv Shankar, Siddhant Garg, and Sunita Sarawagi. 2018.Surprisingly Easy Hard-Attention for Sequence to Sequence Learning. In Proceedings of EMNLP 2018 .

  38. Ankur Bapna, Mia Chen, Orhan Firat, Yuan Cao, and Yonghui Wu. 2018.Training Deeper Neural Machine Translation Models with Transparent Attention. In Proceedings of EMNLP 2018 .

  39. Hareesh Bahuleyan, Lili Mou, Olga Vechtomova, and Pascal Poupart. 2018.Variational Attention for Sequence-to-Sequence Models. In Proceedings of COLING 2018 . (Citation: 14)

  40. Maha Elbayad, Laurent Besacier, and Jakob Verbeek. 2018.Pervasive Attention: 2D Convolutional Neural Networks for Sequence-to-Sequence Prediction. In Proceedings of CoNLL 2018 . (Citation: 4)

  41. Yuntian Deng, Yoon Kim, Justin Chiu, Demi Guo, and Alexander M. Rush. 2018Latent Alignment and Variational Attention. In Proceedings of NeurIPS 2018 . (Citation)

  42. Peyman Passban, Qun Liu, and Andy Way. 2018.Improving Character-Based Decoding Using Target-Side Morphological Information for Neural Machine Translation. In Proceedings of NAACL 2018 . (Citation: 5)

  43. Huadong Chen, Shujian Huang, David Chiang, Xinyu Dai, and Jiajun Chen. 2018.Combining Character and Word Information in Neural Machine Translation Using a Multi-Level Attention. In Proceedings of NAACL 2018 .

  44. Frederick Liu, Han Lu, and Graham Neubig. 2018.Handling Homographs in Neural Machine Translation. In Proceedings of NAACL 2018 . (Citation: 8)

  45. Taku Kudo. 2018.Subword Regularization: Improving Neural Network Translation Models with Multiple Subword Candidates. In Proceedings of ACL 2018 . (Citation: 17)

  46. Makoto Morishita, Jun Suzuki, and Masaaki Nagata. 2018.Improving Neural Machine Translation by Incorporating Hierarchical Subword Features. In Proceedings of COLING 2018 .

  47. Yang Zhao, Jiajun Zhang, Zhongjun He, Chengqing Zong, and Hua Wu. 2018.Addressing Troublesome Words in Neural Machine Translation. In Proceedings of EMNLP 2018 .

  48. Colin Cherry, George Foster, Ankur Bapna, Orhan Firat, and Wolfgang Macherey. 2018.Revisiting Character-Based Neural Machine Translation with Capacity and Compression. In Proceedings of EMNLP 2018 . (Citation: 1)

  49. Rebecca Knowles and Philipp Koehn. 2018.Context and Copying in Neural Machine Translation. In Proceedings of EMNLP 2018 .

  50. Sergey Edunov, Myle Ott, Michael Auli, David Grangier, and Marc’Aurelio Ranzato. 2018.Classical Structured Prediction Losses for Sequence to Sequence Learning. In Proceedings of NAACL 2018 . (Citation: 20)

  51. Zihang Dai, Qizhe Xie, and Eduard Hovy. 2018.From Credit Assignment to Entropy Regularization: Two New Algorithms for Neural Sequence Prediction. In Proceedings of ACL 2018 . (Citation: 1)

  52. Zhen Yang, Wei Chen, Feng Wang, and Bo Xu. 2018.Improving Neural Machine Translation with Conditional Sequence Generative Adversarial Nets. In Proceedings of NAACL 2018 . (Citation: 43)

  53. Kevin Clark, Minh-Thang Luong, Christopher D. Manning, and Quoc Le. 2018.Semi-Supervised Sequence Modeling with Cross-View Training. In Proceedings of EMNLP 2018 .

  54. Lijun Wu, Fei Tian, Tao Qin, Jianhuang Lai, and Tie-Yan Liu. 2018.A Study of Reinforcement Learning for Neural Machine Translation. In Proceedings of EMNLP 2018 . (Citation: 2)

  55. Jason Lee, Elman Mansimov, and Kyunghyun Cho. 2018.Deterministic Non-Autoregressive Neural Sequence Modeling by Iterative Refinement. In Proceedings of EMNLP 2018 .

  56. Semih Yavuz, Chung-Cheng Chiu, Patrick Nguyen, and Yonghui Wu. 2018.CaLcs: Continuously Approximating Longest Common Subsequence for Sequence Level Optimization. In Proceedings of EMNLP 2018 .

  57. Lijun Wu, Fei Tian, Yingce Xia, Yang Fan, Tao Qin, Jianhuang Lai, and Tie-Yan Liu. 2018.Learning to Teach with Dynamic Loss Functions. In Proceedings of NeurIPS 2018 .

  58. Jiatao Gu, James Bradbury, Caiming Xiong, Victor O.K. Li, and Richard Socher. 2018.Non-Autoregressive Neural Machine Translation. In Proceedings of ICLR 2018 . (Citation: 23)

  59. Łukasz Kaiser, Aurko Roy, Ashish Vaswani, Niki Parmar, Samy Bengio, Jakob Uszkoreit, and Noam Shazeer. 2018.Fast Decoding in Sequence Models Using Discrete Latent Variables. In Proceedings of ICML 2018 . (Citation: 3)

  60. Xiangwen Zhang, Jinsong Su, Yue Qin, Yang Liu, Rongrong Ji, and Hongji Wang. 2018.Asynchronous Bidirectional Decoding for Neural Machine Translation. In Proceedings of AAAI 2018 . (Citation: 10)

  61. Jiatao Gu, Daniel Jiwoong Im, and Victor O.K. Li. 2018.Neural machine translation with gumbel-greedy decoding. In Proceedings of AAAI 2018 . (Citation: 5)

  62. Philip Schulz, Wilker Aziz, and Trevor Cohn. 2018.A Stochastic Decoder for Neural Machine Translation. In Proceedings of ACL 2018 . (Citation: 3)

  63. Raphael Shu and Hideki Nakayama. 2018.Improving Beam Search by Removing Monotonic Constraint for Neural Machine Translation. In Proceedings of ACL 2018 .

  64. Junyang Lin, Xu Sun, Xuancheng Ren, Shuming Ma, Jinsong Su, and Qi Su. 2018.Deconvolution-Based Global Decoding for Neural Machine Translation. In Proceedings of COLING 2018 . (Citation: 2)

  65. Chunqi Wang, Ji Zhang, and Haiqing Chen. 2018.Semi-Autoregressive Neural Machine Translation. In Proceedings of EMNLP 2018 .

  66. Xinwei Geng, Xiaocheng Feng, Bing Qin, and Ting Liu. 2018.Adaptive Multi-pass Decoder for Neural Machine Translation. In Proceedings of EMNLP 2018 .

  67. Wen Zhang, Liang Huang, Yang Feng, Lei Shen, and Qun Liu. 2018.Speeding Up Neural Machine Translation Decoding by Cube Pruning. In Proceedings of EMNLP 2018 .

  68. Xinyi Wang, Hieu Pham, Pengcheng Yin, and Graham Neubig. 2018.A Tree-based Decoder for Neural Machine Translation. In Proceedings of EMNLP 2018 . (Citation: 1)

  69. Chenze Shao, Xilin Chen, and Yang Feng. 2018.Greedy Search with Probabilistic N-gram Matching for Neural Machine Translation. In Proceedings of EMNLP 2018 .

  70. Zhisong Zhang, Rui Wang, Masao Utiyama, Eiichiro Sumita, and Hai Zhao. 2018.Exploring Recombination for Efficient Decoding of Neural Machine Translation. In Proceedings of EMNLP 2018 .

  71. Jetic Gū, Hassan S. Shavarani, and Anoop Sarkar. 2018.Top-down Tree Structured Decoding with Syntactic Connections for Neural Machine Translation and Parsing. In Proceedings of EMNLP 2018 .

  72. Yilin Yang, Liang Huang, and Mingbo Ma. 2018.Breaking the Beam Search Curse: A Study of (Re-)Scoring Methods and Stopping Criteria for Neural Machine Translation. In Proceedings of EMNLP 2018 . (Citation: 3)

  73. Yun Chen, Victor O.K. Li, Kyunghyun Cho, and Samuel R. Bowman. 2018.A Stable and Effective Learning Strategy for Trainable Greedy Decoding. In Proceedings of EMNLP 2018 .

2019

  1. Graham Neubig, Zi-Yi Dou, Junjie Hu, Paul Michel, Danish Pruthi, and Xinyi Wang. 2019.compare-mt: A Tool for Holistic Comparison of Language Generation Systems. In Proceedings of NAACL 2019 .
  2. Robert Schwarzenberg, David Harbecke, Vivien Macketanz, Eleftherios Avramidis, and Sebastian Möller. 2019.Train, Sort, Explain: Learning to Diagnose Translation Models. In Proceedings of NAACL 2019 .
  3. Nitika Mathur, Timothy Baldwin, and Trevor Cohn. 2019.Putting Evaluation in Context: Contextual Embeddings Improve Machine Translation Evaluation. In Proceedings of ACL 2019 .
  4. Prathyusha Jwalapuram, Shafiq Joty, Irina Temnikova, and Preslav Nakov. 2019.Evaluating Pronominal Anaphora in Machine Translation: An Evaluation Measure and a Test Suite. In Proceedings of ACL 2019 .
  5. Yikang Shen, Shawn Tan, Alessandro Sordoni, and Aaron Courville. 2019.Ordered Neurons: Integrating Tree Structures into Recurrent Neural Networks. In Proceedings of ICLR 2019 .
  6. Felix Wu, Angela Fan, Alexei Baevski, Yann Dauphin, and Michael Auli. 2019.Pay Less Attention with Lightweight and Dynamic Convolutions. In Proceedings of ICLR 2019 . (Citation: 1)
  7. Mostafa Dehghani, Stephan Gouws, Oriol Vinyals, Jakob Uszkoreit, Lukasz Kaiser. 2019.Universal Transformers. In Proceedings of ICLR 2019 . (Citation: 12)
  8. Zi-Yi Dou, Zhaopeng Tu, Xing Wang, Longyue Wang, Shuming Shi, and Tong Zhang. 2019.Dynamic Layer Aggregation for Neural Machine Translation with Routing-by-Agreement. In Proceedings of AAAI 2019 .
  9. Zihang Dai, Zhilin Yang, Yiming Yang, Jaime Carbonell, Quoc V. Le, and Ruslan Salakhutdinov. 2019.Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context. In Proceedings of ACL 2019 . (Citation: 8)
  10. Wenpeng Yin and Hinrich Schütze. 2019.Attentive Convolution: Equipping CNNs with RNN-style Attention Mechanisms. Transactions of the Association for Computational Linguistics .
  11. Shiv Shankar and Sunita Sarawagi. 2019.Posterior Attention Models for Sequence to Sequence Learning. In Proceedings of ICLR 2019 .
  12. Baosong Yang, Jian Li, Derek Wong, Lidia S. Chao, Xing Wang, and Zhaopeng Tu. 2019.Context-Aware Self-Attention Networks. In Proceedings of AAAI 2019 .
  13. Reza Ghaeini, Xiaoli Z. Fern, Hamed Shahbazi, and Prasad Tadepalli. 2019.Saliency Learning: Teaching the Model Where to Pay Attention. In Proceedings of NAACL 2019 .
  14. Sameen Maruf, André F. T. Martins, and Gholamreza Haffari. 2019.Selective Attention for Context-aware Neural Machine Translation. In Proceedings of NAACL 2019 .
  15. Sainbayar Sukhbaatar, Edouard Grave, Piotr Bojanowski, and Armand Joulin. 2019.Adaptive Attention Span in Transformers. In Proceedings of ACL 2019 .
  16. Yiren Wang, Yingce Xia, Tianyu He, Fei Tian, Tao Qin, ChengXiang Zhai, and Tie-Yan Liu. 2019.Multi-Agent Dual Learning. In Proceedings of ICLR 2019 .
  17. Liqun Chen, Yizhe Zhang, Ruiyi Zhang, Chenyang Tao, Zhe Gan, Haichao Zhang, Bai Li, Dinghan Shen, Changyou Chen, and Lawrence Carin. 2019.Improving Sequence-to-Sequence Learning via Optimal Transport. In Proceedings of ICLR 2019 .
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  30. Jiaming Luo, Yuan Cao, and Regina Barzilay. 2019.Neural Decipherment via Minimum-Cost Flow: from Ugaritic to Linear B. In Proceedings of ACL 2019 .
  31. Yichong Leng, Xu Tan, Tao Qin, Xiang-Yang Li, and Tie-Yan Liu. 2019.Unsupervised Pivot Translation for Distant Languages. In Proceedings of ACL 2019 .
  32. Mikel Artetxe, Gorka Labaka, and Eneko Agirre. 2019.An Effective Approach to Unsupervised Machine Translation. In Proceedings of ACL 2019 .
  33. Mengzhou Xia, Xiang Kong, Antonios Anastasopoulos, and Graham Neubig. 2019.Generalized Data Augmentation for Low-Resource Translation. In Proceedings of ACL 2019 .
  34. Jinhua Zhu, Fei Gao, Lijun Wu, Yingce Xia, Tao Qin, Wengang Zhou, Xueqi Cheng, and Tie-Yan Liu. 2019.Soft Contextual Data Augmentation for Neural Machine Translation. In Proceedings of ACL 2019 .
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  36. Yuanpeng Li, Liang Zhao, Jianyu Wang, and Joel Hestness. 2019.Compositional Generalization for Primitive Substitutions. In Proceedings of EMNLP 2019 .
  37. Yunsu Kim, Petre Petrov, Pavel Petrushkov, Shahram Khadivi, and Hermann Ney. 2019.Pivot-based Transfer Learning for Neural Machine Translation between Non-English Languages. In Proceedings of EMNLP 2019 .
  38. Yunsu Kim, Yingbo Gao, and Hermann Ney. 2019.Effective Cross-lingual Transfer of Neural Machine Translation Models without Shared Vocabularies. In Proceedings of ACL 2019 .
  39. Xu Tan, Yi Ren, Di He, Tao Qin, Zhou Zhao, and Tie-Yan Liu. 2019.Multilingual Neural Machine Translation with Knowledge Distillation. In Proceedings of ICLR 2019 .
  40. Xinyi Wang, Hieu Pham, Philip Arthur, and Graham Neubig. 2019.Multilingual Neural Machine Translation With Soft Decoupled Encoding. In Proceedings of ICLR 2019 .
  41. Maruan Al-Shedivat and Ankur P. Parikh. 2019.Consistency by Agreement in Zero-shot Neural Machine Translation. In Proceedings of NAACL 2019 .
  42. Roee Aharoni, Melvin Johnson, and Orhan Firat. 2019.Massively Multilingual Neural Machine Translation. In Proceedings of NAACL 2019 .
  43. Yunsu Kim, Yingbo Gao, and Hermann Ney. 2019.Effective Cross-lingual Transfer of Neural Machine Translation Models without Shared Vocabularies. In Proceedings of ACL 2019 .
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Tutorial

  1. ACL 2016 Tutorial -- Neural Machine Translation Lmthang在ACL 2016上所做的tutorial [http://nlp.stanford.edu/projects/nmt/Luong-Cho-Manning-NMT-ACL2016-v4.pdf]
  2. 神經機器翻譯前沿進展 由清華大學的劉洋老師在第十二屆全國機器翻譯討論會(2016年8月在烏魯木齊舉辦)上做的報告 [http://nlp.csai.tsinghua.edu.cn/~ly/talks/cwmt2016_ly_v3_160826.pptx]
  3. CCL2016 | T1B: 深度學習與機器翻譯 第十五屆全國計算語言學會議(CCL 2016) [http://www.cips-cl.org/static/CCL2016/tutorialsT1B.html]
  4. Neural Machine Translation [http://statmt.org/mtma16/uploads/mtma16-neural.pdf]
  5. ACL2016上Thang Luong,Kyunghyun Cho和Christopher Manning的講習班 [https://sites.google.com/site/acl16nmt/]
  6. Kyunghyun Cho的talk : New Territory of Machine Translation,主要是講cho自己所關注的NMT問題 [https://drive.google.com/file/d/0B16RwCMQqrtdRVotWlQ3T2ZXTmM/view]

視頻教程

  1. cs224d neural machine translation [https://cs224d.stanford.edu/lectures/CS224d-Lecture15.pdf] [https://www.youtube.com/watch?v=IxQtK2SjWWM&index=11&list=PL3FW7Lu3i5Jsnh1rnUwq_TcylNr7EkRe6]
  2. 清華大學劉洋:基於深度學習的機器翻譯
  3. A Practical Guide to Neural Machine Translation [https://www.youtube.com/watch?v=vxibD6VaOfI]

代碼

  1. seq2seq 實現了穀歌提出的seq2seq模型,基於TensorFlow框架開發。 [https://github.com/tensorflow/tensorflow]
  2. nmt.matlab 由Stanford的博士Lmthang開源的,代碼由Matlab所寫。[https://github.com/lmthang/nmt.matlab]
  3. GroundHog 實現了基於注意力機製的神經機器翻譯模型,由Bengio研究組,基於Theano框架開發。 [https://github.com/lisa-groundhog/GroundHog]
  4. NMT-Coverage 實現了基於覆蓋率的神經機器翻譯模型,由華為諾亞方舟實驗室李航團隊,基於Theano框架開發。 [https://github.com/tuzhaopeng/NMT-Coverage]
  5. OpenNMT 由哈佛大學NLP組開源的神經機器翻譯工具包,基於Torch框架開發,達到工業級程度。 [http://opennmt.net/]
  6. EUREKA-MangoNMT 由中科院自動化所的張家俊老師開發,采用C++。 [https://github.com/jiajunzhangnlp/EUREKA-MangoNMT]
  7. dl4mt-tutorial 基於Theano框架開發。 [https://github.com/nyu-dl/dl4mt-tutorial]

領域專家

  1. Université de Montréal: Yoshua Bengio,Dzmitry Bahdanau
  2. New York University: KyungHyun Cho
  3. Stanford University: Manning,Lmthang
  4. Google: IIya Sutskever,Quoc V.Le
  5. 中科院計算所: 劉群
  6. 東北大學: 朱靖波
  7. 清華大學: 劉洋
  8. 中科院自動化所: 宗成慶,張家俊
  9. 蘇州大學: 熊德意,張民
  10. 華為-諾亞方舟: 李航,塗兆鵬
  11. 百度: 王海峰,吳華

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機器翻譯能夠實現多種語言之間的自動翻譯,方便人類的溝通和交流,具有重要的研究價值。神經機器翻譯(NMT)是一種端到端的自動翻譯係統。統計機器翻譯方法(SMT)曾經是研究最多,最為成功的機器翻譯係統。2014年基於神經網絡的NMT推出以後,由於其存在許多缺陷,發展已經進入低穀。傳統的NMT存在缺乏穩定性,計算耗時等問題。2016年,穀歌公司推出了神經機器翻譯係統GNMT。來自紐約大學Kyunghyun Cho副教授講解了《機器翻譯》最新進展,50頁ppt

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最新論文

Human gender bias is reflected in language and text production. Because state-of-the-art machine translation (MT) systems are trained on large corpora of text, mostly generated by humans, gender bias can also be found in MT. For instance when occupations are translated from a language like English, which mostly uses gender neutral words, to a language like German, which mostly uses a feminine and a masculine version for an occupation, a decision must be made by the MT System. Recent research showed that MT systems are biased towards stereotypical translation of occupations. In 2019 the first, and so far only, challenge set, explicitly designed to measure the extent of gender bias in MT systems has been published. In this set measurement of gender bias is solely based on the translation of occupations. In this paper we present an extension of this challenge set, called WiBeMT, with gender-biased adjectives and adds sentences with gender-biased verbs. The resulting challenge set consists of over 70, 000 sentences and has been translated with three commercial MT systems: DeepL Translator, Microsoft Translator, and Google Translate. Results show a gender bias for all three MT systems. This gender bias is to a great extent significantly influenced by adjectives and to a lesser extent by verbs.

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