A machine translation reading list maintained by Tsinghua Natural Language Processing Group

Machine Translation Reading List

This is a machine translation reading list maintained by the Tsinghua Natural Language Processing Group.

The past three decades have witnessed the rapid development of machine translation, especially for data-driven approaches such as statistical machine translation (SMT) and neural machine translation (NMT). Due to the dominance of NMT at the present time, priority is given to collecting important, up-to-date NMT papers; the Edinburgh/JHU MT research survey wiki has good coverage of older papers and a brief description for each sub-topic of MT. Our list is still incomplete and the categorization might be inappropriate. We will keep adding papers and improving the list. Any suggestions are welcome!

• 10 Must Reads
• Statistical Machine Translation
  • Tutorials
  • Word-based Models
  • Phrase-based Models
  • Syntax-based Models
  • Discriminative Training
  • System Combination
  • Human-centered SMT
    • Interactive SMT
    • Adaptation
• Evaluation
• Neural Machine Translation
  • Tutorials
  • Model Architecture
  • Attention Mechanism
  • Open Vocabulary
  • Training Framework
  • Decoding
  • Low-resource Language Translation
    • Semi-supervised Learning
    • Unsupervised Learning
    • Pivot-based Methods
    • Data Augmentation
    • Data Selection
    • Transfer Learning
    • Meta Learning
  • Multilingual Machine Translation
  • Prior Knowledge Integration
    • Word/Phrase Constraints
    • Syntactic/Semantic Constraints
    • Coverage Constraints
  • Document-level Translation
  • Robustness
  • Interpretability
  • Linguistic Interpretation
  • Fairness and Diversity
  • Efficiency
  • Speech Translation and Simultaneous Translation
  • Multi-modality
  • Ensemble and Reranking
  • Pre-training
  • Domain Adaptation
  • Quality Estimation
  • Human-centered NMT
    • Interactive NMT
    • Automatic Post-Editing
  • Poetry Translation
  • Eco-friendly
• Word Translation (Bilingual Lexicon Induction)
• WMT Winners
  • WMT 2019
  • WMT 2018
  • WMT 2017
  • WMT 2016

• Peter E. Brown, Stephen A. Della Pietra, Vincent J. Della Pietra, and Robert L. Mercer. 1993. The Mathematics of Statistical Machine Translation: Parameter Estimation. Computational Linguistics. (Citation: 5,218)
• Kishore Papineni, Salim Roukos, Todd Ward, and Wei-Jing Zhu. 2002. BLEU: a Method for Automatic Evaluation of Machine Translation. In Proceedings of ACL 2002. (Citation: 10,700)
• Philipp Koehn, Franz J. Och, and Daniel Marcu. 2003. Statistical Phrase-Based Translation. In Proceedings of NAACL 2003. (Citation: 3,713)
• Franz Josef Och. 2003. Minimum Error Rate Training in Statistical Machine Translation. In Proceedings of ACL 2003. (Citation: 3,115)
• David Chiang. 2007. Hierarchical Phrase-Based Translation. Computational Linguistics. (Citation: 1,235)
• Ilya Sutskever, Oriol Vinyals, and Quoc V. Le. 2014. Sequence to Sequence Learning with Neural Networks. In Proceedings of NIPS 2014. (Citation: 9,432)
• Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio. 2015. Neural Machine Translation by Jointly Learning to Align and Translate. In Proceedings of ICLR 2015. (Citation: 10,479)
• Diederik P. Kingma, Jimmy Ba. 2015. Adam: A Method for Stochastic Optimization. In Proceedings of ICLR 2015. (Citation: 37,480)
• Rico Sennrich, Barry Haddow, and Alexandra Birch. 2016. Neural Machine Translation of Rare Words with Subword Units. In Proceedings of ACL 2016. (Citation: 1,679)
• Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser, and Illia Polosukhin. 2017. Attention is All You Need. In Proceedings of NIPS 2017. (Citation: 6,112)

• Philipp Koehn. 2006. Statistical Machine Translation: the Basic, the Novel, and the Speculative. EACL 2006 Tutorial. (Citation: 10)
• Adam Lopez. 2008. Statistical Machine Translation. ACM Computing Surveys. (Citation: 373)

• Peter E. Brown, Stephen A. Della Pietra, Vincent J. Della Pietra, and Robert L. Mercer. 1993. The Mathematics of Statistical Machine Translation: Parameter Estimation. Computational Linguistics. (Citation: 4,965)
• Stephan Vogel, Hermann Ney, and Christoph Tillmann. 1996. HMM-Based Word Alignment in Statistical Translation. In Proceedings of COLING 1996. (Citation: 940)
• Franz Josef Och and Hermann Ney. 2003. A Systematic Comparison of Various Statistical Alignment Models. Computational Linguistics. (Citation: 3,980)
• Percy Liang, Ben Taskar, and Dan Klein. 2006. Alignment by Agreement. In Proceedings of NAACL 2006. (Citation: 452)
• Chris Dyer, Victor Chahuneau, and Noah A. Smith. 2013. A Simple, Fast, and Effective Reparameterization of IBM Model 2. In Proceedings of NAACL 2013. (Citation: 310)

• Philipp Koehn, Franz J. Och, and Daniel Marcu. 2003. Statistical Phrase-Based Translation. In Proceedings of NAACL 2003. (Citation: 3,516)
• Michel Galley and Christopher D. Manning. 2008. A Simple and Effective Hierarchical Phrase Reordering Model. In Proceedings of EMNLP 2008. (Citation: 275)

• Dekai Wu. 1997. Stochastic Inversion Transduction Grammars and Bilingual Parsing of Parallel Corpora. Computational Linguistics. (Citation: 1,009)
• Michel Galley, Jonathan Graehl, Kevin Knight, Daniel Marcu, Steve DeNeefe, Wei Wang, and Ignacio Thayer. 2006. Scalable Inference and Training of Context-Rich Syntactic Translation Models. In Proceedings of COLING/ACL 2006. (Citation: 475)
• Yang Liu, Qun Liu, and Shouxun Lin. 2006. Tree-to-String Alignment Template for Statistical Machine Translation. In Proceedings of COLING/ACL 2006. (Citation: 391)
• Deyi Xiong, Qun Liu, and Shouxun Lin. 2006. Maximum Entropy Based Phrase Reordering Model for Statistical Machine Translation. In Proceedings of COLING/ACL 2006. (Citation: 299)
• David Chiang. 2007. Hierarchical Phrase-Based Translation. Computational Linguistics. (Citation: 1,192)
• Liang Huang and David Chiang. 2007. Forest Rescoring: Faster Decoding with Integrated Language Models. In Proceedings of ACL 2007. (Citation: 280)
• Haitao Mi, Liang Huang, and Qun Liu. 2008. Forest-based Translation. In Proceedings of ACL 2008. (Citation: 239)
• Min Zhang, Hongfei Jiang, Aiti Aw, Haizhou Li, Chew Lim Tan, and Sheng Li. 2008. A Tree Sequence Alignment-based Tree-to-Tree Translation Model. In Proceedings of ACL 2008. (Citation: 124)
• Libin Shen, Jinxi Xu, and Ralph Weischedel. 2008. A New String-to-Dependency Machine Translation Algorithm with a Target Dependency Language Model. In Proceedings of ACL 2008. (Citation: 278)
• Haitao Mi and Liang Huang. 2008. Forest-based Translation Rule Extraction. In Proceedings of EMNLP 2008. (Citation: 239)
• Yang Liu, Yajuan Lü, and Qun Liu. 2009. Improving Tree-to-Tree Translation with Packed Forests. In Proceedings of ACL/IJNLP 2009. (Citation: 93)
• David Chiang. 2010. Learning to Translate with Source and Target Syntax. In Proceedings of ACL 2010. (Citation: 118)

• Franz Josef Och and Hermann Ney. 2002. Discriminative Training and Maximum Entropy Models for Statistical Machine Translation. In Proceedings of ACL 2002. (Citation: 1,258)
• Franz Josef Och. 2003. Minimum Error Rate Training in Statistical Machine Translation. In Proceedings of ACL 2003. (Citation: 2,984)
• Taro Watanabe, Jun Suzuki, Hajime Tsukada, and Hideki Isozaki. 2007. Online Large-Margin Training for Statistical Machine Translation. In Proceedings of EMNLP-CoNLL 2007. (Citation: 197)
• David Chiang, Kevin Knight, and Wei Wang. 2009. 11,001 New Features for Statistical Machine Translation. In Proceedings of NAACL 2009. (Citation: 251)

• Antti-Veikko Rosti, Spyros Matsoukas, and Richard Schwartz. 2007. Improved Word-Level System Combination for Machine Translation. In Proceedings of ACL 2007. (Citation: 144)
• Xiaodong He, Mei Yang, Jianfeng Gao, Patrick Nguyen, and Robert Moore. 2008. Indirect-HMM-based Hypothesis Alignment for Combining Outputs from Machine Translation Systems. In Proceedings of EMNLP 2008. (Citation: 96)

• George Foster, Pierre Isabelle and Pierre Plamondon. 1997. Target-text mediated interactive machine translation. Machine Translation. (Citation: 116)
• Philippe Langlais, Guy Lapalme and Marie Lorange. 2002. TransType: Development-Evaluation Cycles to Boost Translator’s Productivity. Machine Translation. (Citation: 74)
• Jesús Tomas and Francisco Casacuberta. 2006. Statistical phrase-based models for interactive computer-assisted translation. In Proceedings of COLING/ACL. (Citation: 31)
• Enrique Vidal, Francisco Casacuberta, Luis Rodríguez-Ruiz, Jorge Civera, Carlos D. Martínez-Hinarejos. 2006. Computer-Assisted Translation Using Speech Recognition. IEEE Transaction on Audio, Speech and Language Processing. (Citation: 62)
• Shahram Khadivi and Hermann Ney. 2008. Integration of Speech Recognition and Machine Translation in Computer-Assisted Translation. IEEE Transaction on Audio, Speech and Language Processing. (Citation: 30)
• Sergio Barrachina, Oliver Bender, Francisco Casacuberta, Jorge Civera, Elsa Cubel, Shahram Khadivi, Antonio L. Lagarda, Hermann Ney, Jesús Tomás and Enrique Vidal. 2009. Statistical approaches to computer-assisted translation. Computational Linguistics. (Citation: 207)
• Francisco Casacuberta, Jorge Civera, Elsa Cubel, Antonio L. Lagarda, Guy Lapalme, Elliott Macklovitch, Enrique Vidal. 2009. Human interaction for high quality machine translation. Communications of the ACM. (Citation: 49)
• Vicent Alabau, Alberto Sanchis and Francisco Casacuberta. 2014. Improving on-line handwritten recognition in interactive machine translation. Pattern Recognition. (Citation: 18)
• Shanbo Cheng, Shujian Huang, Huadong Chen, Xin-Yu Dai and Jiajun Chen. 2016. PRIMT: A Pick-Revise Framework for Interactive Machine Translation. In Proceedings of NAACL 2016. (Citation: 9)
• Miguel Domingo, Álvaro Peris and Francisco Casacuberta. 2018. Segment-based interactive-predictive machine translation. Machine Translation. (Citation: 2)

• Pascual Martínez-Gómez, Germán Sanchis-Trilles and Francisco Casacuberta. 2012. Online adaptation strategies for statistical machine translation in post-editing scenarios. Pattern Recognition. (Citation: 40)
• Jesús González-Rubio and Francisco Casacuberta. 2014. Cost-Sensitive Active Learning for Computer-Assisted Translation. Pattern Recognition Letters. (Citation: 11)
• Antonio L. Lagarda, Daniel Ortiz-Martínez, Vicent Alabau and Francisco Casacuberta. 2015. Translating without in-domain corpus: Machine translation post-editing with online learning techniques. Computer Speech & Language. (Citation: 10)
• Germán Sanchis-Trilles, Francisco Casacuberta. 2015. Improving translation quality stability using Bayesian predictive adaptation. Computer Speech & Language. (Citation: 1)
• Daniel Ortiz-Martínez. 2016. Online Learning for Statistical Machine Translation. Computational Linguistics. (Citation: 13)

• Kishore Papineni, Salim Roukos, Todd Ward, and Wei-Jing Zhu. 2002. BLEU: a Method for Automatic Evaluation of Machine Translation. In Proceedings of ACL 2002. (Citation: 8,499)
• Philipp Koehn. 2004. Statistical Significance Tests for Machine Translation Evaluation. In Proceedings of EMNLP 2004. (Citation: 1,015)
• 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. (Citation: 1,355)
• Matthew Snover and Bonnie Dorr, Richard Schwartz, Linnea Micciulla, and John Makhoul. 2006. A Study of Translation Edit Rate with Targeted Human Annotation. In Proceedings of AMTA 2006. (Citation: 1,713)
• Maja Popovic. 2015. chrF: Character n-gram F-score for Automatic MT Evaluation. In Proceedings of WMT 2015. (Citation: 58)
• 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)
• Arun Tejasvi Chaganty, Stephen Mussman, and Percy Liang. 2018. The price of debiasing automatic metrics in natural language evaluation. In Proceedings of ACL 2018.
• 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.
• 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.
• Nitika Mathur, Timothy Baldwin, and Trevor Cohn. 2019. Putting Evaluation in Context: Contextual Embeddings Improve Machine Translation Evaluation. In Proceedings of ACL 2019.
• 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.

• Thang Luong, Kyunghyun Cho, and Christopher Manning. 2016. Neural Machine Translation. ACL 2016 Tutorial.
• Graham Neubig. 2017. Neural Machine Translation and Sequence-to-sequence Models: A Tutorial. arXiv:1703.01619. (Citation: 45)
• Oriol Vinyals and Navdeep Jaitly. 2017. Seq2Seq ICML Tutorial. ICML 2017 Tutorial.
• Philipp Koehn. 2017. Neural Machine Translation. arxiv:1709.07809.
• Philipp Koehn and Rebecca Knowles. 2017. Six Challenges for Neural Machine Translation. In Proceedings of the First Workshop on Neural Machine Translation. (Citation: 121)

• Nal Kalchbrenner and Phil Blunsom. 2013. Recurrent Continuous Translation Models. In Proceedings of EMNLP 2013. (Citation: 623)
• Ilya Sutskever, Oriol Vinyals, and Quoc V. Le. 2014. Sequence to Sequence Learning with Neural Networks. In Proceedings of NIPS 2014. (Citation: 5,452)
• Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio. 2015. Neural Machine Translation by Jointly Learning to Align and Translate. In Proceedings of ICLR 2015. (Citation: 5,596)
• Yonghui Wu, Mike Schuster, Zhifeng Chen, Quoc V. Le, Mohammad Norouzi, Wolfgang Macherey, Maxim Krikun, Yuan Cao, Qin Gao, Klaus Macherey, Jeff Klingner, Apurva Shah, Melvin Johnson, Xiaobing Liu, Łukasz Kaiser, Stephan Gouws, Yoshikiyo Kato, Taku Kudo, Hideto Kazawa, Keith Stevens, George Kurian, Nishant Patil, Wei Wang, Cliff Young, Jason Smith, Jason Riesa, Alex Rudnick, Oriol Vinyals, Greg Corrado, Macduff Hughes, and Jeffrey Dean. 2016. Google’s Neural Machine Translation System: Bridging the Gap between Human and Machine Translation. In Proceedings of NIPS 2016. (Citation: 1,046)
• Jie Zhou, Ying Cao, Xuguang Wang, Peng Li, and Wei Xu. 2016. Deep Recurrent Models with Fast-Forward Connections for Neural Machine Translation. Transactions of the Association for Computational Linguistics. (Citation: 73)
• Jiatao Gu, Zhengdong Lu, Hang Li, and Victor O.K. Li. 2016. Incorporating Copying Mechanism in Sequence-to-Sequence Learning. In Proceedings of ACL 2016. (Citation: 254)
• Biao Zhang, Deyi Xiong, Jinsong Su, Hong Duan, and Min Zhang. 2016. Variational Neural Machine Translation. In Proceedings of EMNLP 2016. (Citation: 38)
• Nal Kalchbrenner, Lasse Espeholt, Karen Simonyan, Aaron van den Oord, Alex Graves, and Koray Kavukcuoglu. 2016. Neural Machine Translation in Linear Time. arXiv:1610.10099. (Citation: 189)
• Jonas Gehring, Michael Auli, David Grangier, Denis Yarats, and Yann N. Dauphin. 2017. Convolutional Sequence to Sequence Learning. In Proceedings of ICML 2017. (Citation: 453)
• Jonas Gehring, Michael Auli, David Grangier, and Yann Dauphin. 2017. A Convolutional Encoder Model for Neural Machine Translation. In Proceedings of ACL 2017. (Citation: 85)
• Mingxuan Wang, Zhengdong Lu, Jie Zhou, and Qun Liu. 2017. Deep Neural Machine Translation with Linear Associative Unit. In Proceedings of ACL 2017. (Citation: 21)
• Matthias Sperber, Graham Neubig, Jan Niehues, and Alex Waibel. 2017. Neural Lattice-to-Sequence Models for Uncertain Inputs. In Proceedings of EMNLP 2017. (Citation: 11)
• Denny Britz, Anna Goldie, Minh-Thang Luong, and Quoc Le. 2017. Massive Exploration of Neural Machine Translation Architectures. In Proceedings of EMNLP 2017. (Citation: 114)
• Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser, and Illia Polosukhin. 2017. Attention is All You Need. In Proceedings of NIPS 2017. (Citation: 1,748)
• Yingce Xia, Fei Tian, Lijun Wu, Jianxin Lin, Tao Qin, Nenghai Yu, and Tie-Yan Liu. 2017. Deliberation Networks: Sequence Generation Beyond One-Pass Decoding. In Proceedings of NIPS 2017. (Citation: 38)
• Zhaopeng Tu, Yang Liu, Lifeng Shang, Xiaohua Liu, and Hang Li. 2017. Neural machine translation with reconstruction. In Proceedings of AAAI 2017. (Citation: 75)
• Lukasz Kaiser, Aidan N. Gomez, and Francois Chollet. 2018. Depthwise Separable Convolutions for Neural Machine Translation. In Proceedings of ICLR 2018. (Citation: 27)
• 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)
• 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)
• 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)
• 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)
• Jingjing Gong, Xipeng Qiu, Shaojing Wang, and Xuanjing Huang. 2018. Information Aggregation via Dynamic Routing for Sequence Encoding. In COLING 2018.
• 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.
• Yachao Li, Junhui Li, and Min Zhang. 2018. Adaptive Weighting for Neural Machine Translation. In Proceedings of COLING 2018.
• 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.
• 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)
• 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.
• 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)
• Ke Tran, Arianna Bisazza, and Christof Monz. 2018. The Importance of Being Recurrent for Modeling Hierarchical Structure. In Proceedings of EMNLP 2018. (Citation: 6)
• 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)
• 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)
• Harshil Shah and David Barber. 2018. Generative Neural Machine Translation. In Proceedings of NeurIPS 2018.
• 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)
• Yikang Shen, Shawn Tan, Alessandro Sordoni, and Aaron Courville. 2019. Ordered Neurons: Integrating Tree Structures into Recurrent Neural Networks. In Proceedings of ICLR 2019.
• 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)
• Mostafa Dehghani, Stephan Gouws, Oriol Vinyals, Jakob Uszkoreit, Lukasz Kaiser. 2019. Universal Transformers. In Proceedings of ICLR 2019. (Citation: 12)
• 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.
• 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)
• Qipeng Guo, Xipeng Qiu, Pengfei Liu, Yunfan Shao, Xiangyang Xue, and Zheng Zhang. 2019. Star-Transformer. In Proceedings of NAACL 2019.
• Sho Takase and Naoaki Okazaki. 2019. Positional Encoding to Control Output Sequence Length. In Proceedings of NAACL 2019.
• Jian Li, Baosong Yang, Zi-Yi Dou, Xing Wang, Michael R. Lyu, and Zhaopeng Tu. 2019. Information Aggregation for Multi-Head Attention with Routing-by-Agreement. In Proceedings of NAACL 2019.
• Baosong Yang, Longyue Wang, Derek Wong, Lidia S. Chao, and Zhaopeng Tu. 2019. Convolutional Self-Attention Networks. In Proceedings of NAACL 2019.
• Jie Hao, Xing Wang, Baosong Yang, Longyue Wang, Jinfeng Zhang, and Zhaopeng Tu. 2019. Modeling Recurrence for Transformer. In Proceedings of NAACL 2019.
• Nikolaos Pappas and James Henderson. 2019. Deep Residual Output Layers for Neural Language Generation. In Proceedings of ICML 2019.
• David R. So, Chen Liang, and Quoc V. Le. 2019. The Evolved Transformer. In Proceedings of ICML 2019.
• Ben Peters, Vlad Niculae, and André F.T. Martins. 2019. Sparse Sequence-to-Sequence Models. In Proceedings of ACL 2019.
• Roberto Dessì and Marco Baroni. 2019. CNNs found to jump around more skillfully than RNNs: Compositional generalization in seq2seq convolutional networks. In Proceedings of ACL 2019.
• Sainbayar Sukhbaatar, Edouard Grave, Piotr Bojanowski, and Armand Joulin. 2019. Adaptive Attention Span in Transformers. In Proceedings of ACL 2019.
• Yi Tay, Aston Zhang, Luu Anh Tuan, Jinfeng Rao, Shuai Zhang, Shuohang Wang, Jie Fu, and Siu Cheung Hui. 2019. Lightweight and Efficient Neural Natural Language Processing with Quaternion Networks. In Proceedings of ACL 2019.
• Qiang Wang, Bei Li, Tong Xiao, Jingbo Zhu, Changliang Li, Derek F. Wong, and Lidia S. Chao. 2019. Learning Deep Transformer Models for Machine Translation. In Proceedings of ACL 2019.
• Fengshun Xiao, Jiangtong Li, Hai Zhao, Rui Wang, and Kehai Chen. 2019. Lattice-Based Transformer Encoder for Neural Machine Translation. In Proceedings of ACL 2019.
• Matthias Sperber, Graham Neubig, Ngoc-Quan Pham, and Alex Waibel. 2019. Self-Attentional Models for Lattice Inputs. In Proceedings of ACL 2019.
• Xing Wang, Zhaopeng Tu, Longyue Wang, and Shuming Shi. 2019. Exploiting Sentential Context for Neural Machine Translation. In Proceedings of ACL 2019.
• Kris Korrel, Dieuwke Hupkes, Verna Dankers, and Elia Bruni. 2019. Transcoding compositionally: using attention to find more generalizable solutions. In Proceedings of ACL 2019.
• Lijun Wu, Yiren Wang, Yingce Xia, Fei Tian, Fei Gao, Tao Qin, Jianhuang Lai, and Tie-Yan Liu. 2019. Depth Growing for Neural Machine Translation. In Proceedings of ACL 2019.
• Shaojie Bai, J. Zico Kolter, and Vladlen Koltun. 2019. Deep Equilibrium Models. In Proceedings of NeurIPS 2019.
• Gonçalo M. Correia, Vlad Niculae, and André F.T. Martins. 2019. Adaptively Sparse Transformers. In Proceedings of EMNLP 2019.
• Xuezhe Ma, Chunting Zhou, Xian Li, Graham Neubig, and Eduard Hovy. 2019. FlowSeq: Non-Autoregressive Conditional Sequence Generation with Generative Flow. In Proceedings of EMNLP 2019.
• Yau-Shian Wang, Hung-Yi Lee, and Yun-Nung Chen. 2019. Tree Transformer: Integrating Tree Structures into Self-Attention. In Proceedings of EMNLP 2019.
• Mingxuan Wang, Jun xie, Zhixing Tan, Jinsong Su, Deyi Xiong and Lei Li. 2019. Towards Linear Time Neural Machine Translation with Capsule Networks. In Proceedings of EMNLP 2019.
• Biao Zhang, Ivan Titov and Rico Sennrich. 2019. Improving Deep Transformer with Depth-Scaled Initialization and Merged Attention. In Proceedings of EMNLP 2019.
• Jiatao Gu, Changhan Wang, Junbo Zhao. 2019. Levenshtein Transformer. In Proceedings of NeurIPS 2019.
• Xin Sheng, Linli Xu, Junliang Guo, Jingchang Liu, Ruoyu Zhao and Yinlong Xu. 2020. IntroVNMT: An Introspective Model for Variational Neural Machine Translation. In Proceedings of AAAI 2020.
• Jian Li, Xing Wang, Baosong Yang, Shuming Shi, Michael R. Lyu and Zhaopeng Tu. 2020. Neuron Interaction Based Representation Composition for Neural Machine Translation. In Proceedings of AAAI 2020.
• Benyou Wang, Donghao Zhao, Christina Lioma, Qiuchi Li, Peng Zhang, Jakob Grue Simonsen. 2020. Encoding word order in complex embeddings. In Proceedings of ICLR 2020.
• Nikita Kitaev, Lukasz Kaiser, Anselm Levskaya. 2020. Reformer: The Efficient Transformer. In Proceedings of ICLR 2020.
• Maha Elbayad, Jiatao Gu, Edouard Grave, Michael Auli. 2020. Depth-Adaptive Transformer. In Proceedings of ICLR 2020.

• Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio. 2015. Neural Machine Translation by Jointly Learning to Align and Translate. In Proceedings of ICLR 2015. (Citation: 5,596)
• Minh-Thang Luong, Hieu Pham, and Christopher D. Manning. 2015. Effective Approaches to Attention-based Neural Machine Translation. In Proceedings of EMNLP 2015. (Citation: 1,466)
• Shi Feng, Shujie Liu, Nan Yang, Mu Li, Ming Zhou, and Kenny Q. Zhu. 2016. Improving Attention Modeling with Implicit Distortion and Fertility for Machine Translation. In Proceedings of COLING 2016. (Citation: 18)
• Haitao Mi, Zhiguo Wang, and Abe Ittycheriah. 2016. Supervised Attentions for Neural Machine Translation. In Proceedings of EMNLP 2016. (Citation: 43)
• Zhouhan Lin, Minwei Feng, Cicero Nogueira dos Santos, Mo Yu, Bing Xiang, Bowen Zhou, and Yoshua Bengio. 2017. A Structured Self-attentive Sentence Embedding. In Proceedings of ICLR 2017. (Citation: 216)
• 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)
• 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)
• 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)
• Peter Shaw, Jakob Uszkorei, and Ashish Vaswani. 2018. Self-Attention with Relative Position Representations. In Proceedings of NAACL 2018. (Citation: 24)
• 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)
• 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.
• Biao Zhang, Deyi Xiong, and Jinsong Su. 2018. Accelerating Neural Transformer via an Average Attention Network. In Proceedings of ACL 2018. (Citation: 5)
• Tobias Domhan. 2018. How Much Attention Do You Need? A Granular Analysis of Neural Machine Translation Architectures. In Proceedings of ACL 2018. (Citation: 3)
• 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)
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WMT is the most important annual international competition on machine translation. We collect the competition results on the news translation task since WMT 2016 (the First Conference of Machine Translation) and summarize the techniques used in the systems with the top performance. Currently, we focus on four directions: ZH-EN, EN-ZH, DE-EN, and EN-DE. The summarized algorithms might be incomplete; your suggestions are welcome!

• The winner of ZH-EN, DE-EN and EN-DE: Microsoft

  • System report: Yingce Xia, Xu Tan, Fei Tian, Fei Gao, Di He, Weicong Chen, Yang Fan, Linyuan Gong, Yichong Leng, Renqian Luo, Yiren Wang, Lijun Wu, Jinhua Zhu, Tao Qin and Tie-Yan Liu. 2019. Microsoft Research Asia’s Systems for WMT19. In Proceedings of the Fourth Conference on Machine Translation: Shared Task Papers.
  • News: Microsoft Research Asia (MSRA) leads in 2019 WMT international machine translation competition
  • Techniques:
    • 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.
    • Kaitao Song, Xu Tan, Tao Qin, Jianfeng Lu, and Tie-Yan Liu. 2019. MASS: Masked Sequence to Sequence Pre-training for Language Generation. In Proceedings of ICML 2019.
    • Renqian Luo, Fei Tian, Tao Qin, Enhong Chen, and Tie-Yan Liu. 2018. Neural Architecture Optimization. In Proceedings of NeurIPS 2018.
    • Jinhua Zhu, Fei Gao, Lijun Wu, Yingce Xia, Tao Qin, Wengang Zhou, Xueqi Cheng, Tie-Yan Liu. 2019. Soft Contextual Data Augmentation for Neural Machine Translation. In Proceedings of ACL 2019.

• The winner of EN-ZH: PATECH

  • System report: Coming soon…
  • Techniques: Transformer + Back-Translation + Reranking + Ensemble

• The winner of ZH-EN: Tencent

  • System report: Mingxuan Wang, Li Gong, Wenhuan Zhu, Jun Xie, and Chao Bian. 2018. Tencent Neural Machine Translation Systems for WMT18. In Proceedings of the Third Conference on Machine Translation: Shared Task Papers.
  • Techniques: RNMT + Transformer + BPE + Rerank ensemble outputs with 48 features (including t2t R2l, t2t L2R, rnn L2R, rnn R2L etc.) + Back Translation + Joint Train with English to Chinese systems + Fine-tuning with selected data + Knowledge distillation

• The winner of EN-ZH: GTCOM

  • System report: Chao Bei, Hao Zong, Yiming Wang, Baoyong Fan, Shiqi Li, and Conghu Yuan. 2018. An Empirical Study of Machine Translation for the Shared Task of WMT18. In Proceedings of the Third Conference on Machine Translation: Shared Task Papers.
  • Techniques: Transformer + Back-Translation + Data Filtering by rules, language models and translation models + BPE + Greedy Ensemble Decoding + Fine-Tuning with newstest2017 back translation

• The winner of DE-EN: RWTH Aachen University

  • System report: Julian Schamper, Jan Rosendahl, Parnia Bahar, Yunsu Kim, Arne Nix, and Hermann Ney. 2018. The RWTH Aachen University Supervised Machine Translation Systems for WMT 2018. In Proceedings of the Third Conference on Machine Translation: Shared Task Papers.
  • Techniques: Ensemble of 3-strongest Transformer models + Data Selection + BPE + Fine-Tuning + Important Hyperparameters (batch size and model dimension)

• The winner of EN-DE: Microsoft

  • System report: Marcin Junczys-Dowmunt. 2018. Microsoft’s Submission to the WMT2018 News Translation Task: How I Learned to Stop Worrying and Love the Data. In Proceedings of the Third Conference on Machine Translation: Shared Task Papers.
  • Techniques: Marian + Transformer-big + BPE + Ensemble + Data Filtering + Domain-Weighted {ParaCrawl, original data} + Decoder-time ensemble with in-domain Transformer-style language model + Reranking with Right-to-left Transformer-big models

• The winner of ZH-EN: Sogou

  • System report: Yuguang Wang, Shanbo Cheng, Liyang Jiang, Jiajun Yang, Wei Chen, Muze Li, Lin Shi, Yanfeng Wang, and Hongtao Yang. 2017. Sogou Neural Machine Translation Systems for WMT17. In Proceedings of the Second Conference on Machine Translation: Shared Task Papers.
  • Techniques: Encoder-Decoder with Attention + BPE + Reranking (R2L, T2S, N-gram language models) + Tagging Model + Name Entity Translation + Ensemble

• The winner of EN-ZH, DE-EN and EN-DE: University of Edinburgh

  • System report: Rico Sennrich, Alexandra Birch, Anna Currey, Ulrich Germann, Barry Haddow, Kenneth Heafield, Antonio Valerio Miceli Barone, and Philip Williams. 2017. The University of Edinburgh’s Neural MT Systems for WMT17. In Proceedings of the Second Conference on Machine Translation: Shared Task Papers.
  • Techniques: Encoder-Decoder with Attention + Deep Model + Layer Normalization + Weight Tying + Back-Translation + BPE + Reranking(L2R, R2L) + Ensemble

• The winner of DE-EN: University of Regensburg

  • System report: Failed to find it
  • Techniques: Failed to find it

• The winner of EN-DE: University of Edinburgh

  • System report: Edinburgh Neural Machine Translation Systems for WMT 16. In Proceedings of the First Conference on Machine Translation: Shared Task Papers.
  • Techniques: Encoder-Decoder with Attention + Back-Translation + BPE + Reranking(R2L) + Ensemble