机器翻译,又称为自动翻译,是利用计算机将一种自然语言(源语言)转换为另一种自然语言(目标语言)的过程。它是计算语言学的一个分支,是人工智能的终极目标之一,具有重要的科学研究价值。

机器翻译 Machine Translation 专知荟萃

入门学习

  1. CIPS青工委学术专栏第9期 | 神经机器翻译 http://www.cipsc.org.cn/qngw/?p=953
  2. 基于深度学习的机器翻译研究进展 http://www.caai.cn/index.php?s=/Home/Article/qikandetail/year/2016/month/02.html
  3. 35张PPT带你深入浅出认识,深度学习的机器翻译 (也有视频教程) http://mp.weixin.qq.com/s/pnJDuXxw2VI9zEWgNivKdw
  4. Kyunghyun Cho对神经机器翻译的介绍 [https://devblogs.nvidia.com/parallelforall/introduction-neural-machine-translation-with-gpus/] [http://devblogs.nvidia.com/parallelforall/introduction-neural-machine-translation-gpus-part-2/] [https://devblogs.nvidia.com/parallelforall/introduction-neural-machine-translation-gpus-part-3/]
  5. 神经网络机器翻译Neural Machine Translation(1): Encoder-Decoder Architecture (2): Attention Mechanism [http://blog.csdn.net/u011414416/article/details/51048994] [http://blog.csdn.net/u011414416/article/details/51057789]
  6. TensorFlow 神经机器翻译教程 [https://github.com/tensorflow/nmt]
  7. AMTA2016上Rico Sennrich的讲习班 http://statmt.org/mtma16/uploads/mtma16-neural.pdf

进阶论文

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. 2018 Latent 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 .
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  10. Wenpeng Yin and Hinrich Schütze. 2019. Attentive Convolution: Equipping CNNs with RNN-style Attention Mechanisms. Transactions of the Association for Computational Linguistics .
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综述

  1. 神经机器翻译前沿进展 清华大学刘洋老师 [http://crad.ict.ac.cn/CN/abstract/abstract3422.shtml]
  2. 斯坦福Thang Luong的博士论文 [https://github.com/lmthang/thesis/blob/master/thesis.pdf]
  3. Deep Neural Networks in Machine Translation: An Overview [http://www.nlpr.ia.ac.cn/cip/ZongPublications/2015/IEEE-Zhang-8-5.pdf]

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. 神经机器翻译前沿进展 由清华大学的刘洋老师在第十二届全国机器翻译讨论会(2016年8月在乌鲁木齐举办)上做的报告 [http://nlp.csai.tsinghua.edu.cn/~ly/talks/cwmt2016_ly_v3_160826.pptx]
  3. CCL2016 | T1B: 深度学习与机器翻译 第十五届全国计算语言学会议(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的讲习班 [https://sites.google.com/site/acl16nmt/]
  6. Kyunghyun Cho的talk : New Territory of Machine Translation,主要是讲cho自己所关注的NMT问题 [https://drive.google.com/file/d/0B16RwCMQqrtdRVotWlQ3T2ZXTmM/view]

视频教程

  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. 清华大学刘洋:基于深度学习的机器翻译
  3. A Practical Guide to Neural Machine Translation [https://www.youtube.com/watch?v=vxibD6VaOfI]

代码

  1. seq2seq 实现了谷歌提出的seq2seq模型,基于TensorFlow框架开发。 [https://github.com/tensorflow/tensorflow]
  2. nmt.matlab 由Stanford的博士Lmthang开源的,代码由Matlab所写。[https://github.com/lmthang/nmt.matlab]
  3. GroundHog 实现了基于注意力机制的神经机器翻译模型,由Bengio研究组,基于Theano框架开发。 [https://github.com/lisa-groundhog/GroundHog]
  4. NMT-Coverage 实现了基于覆盖率的神经机器翻译模型,由华为诺亚方舟实验室李航团队,基于Theano框架开发。 [https://github.com/tuzhaopeng/NMT-Coverage]
  5. OpenNMT 由哈佛大学NLP组开源的神经机器翻译工具包,基于Torch框架开发,达到工业级程度。 [http://opennmt.net/]
  6. EUREKA-MangoNMT 由中科院自动化所的张家俊老师开发,采用C++。 [https://github.com/jiajunzhangnlp/EUREKA-MangoNMT]
  7. dl4mt-tutorial 基于Theano框架开发。 [https://github.com/nyu-dl/dl4mt-tutorial]

领域专家

  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. 中科院计算所: 刘群
  6. 东北大学: 朱靖波
  7. 清华大学: 刘洋
  8. 中科院自动化所: 宗成庆,张家俊
  9. 苏州大学: 熊德意,张民
  10. 华为-诺亚方舟: 李航,涂兆鹏
  11. 百度: 王海峰,吴华

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