Hao Xiong


2019

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Multi-agent Learning for Neural Machine Translation
Tianchi Bi | Hao Xiong | Zhongjun He | Hua Wu | Haifeng Wang
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)

Conventional Neural Machine Translation (NMT) models benefit from the training with an additional agent, e.g., dual learning, and bidirectional decoding with one agent decod- ing from left to right and the other decoding in the opposite direction. In this paper, we extend the training framework to the multi-agent sce- nario by introducing diverse agents in an in- teractive updating process. At training time, each agent learns advanced knowledge from others, and they work together to improve translation quality. Experimental results on NIST Chinese-English, IWSLT 2014 German- English, WMT 2014 English-German and large-scale Chinese-English translation tasks indicate that our approach achieves absolute improvements over the strong baseline sys- tems and shows competitive performance on all tasks.

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STACL: Simultaneous Translation with Implicit Anticipation and Controllable Latency using Prefix-to-Prefix Framework
Mingbo Ma | Liang Huang | Hao Xiong | Renjie Zheng | Kaibo Liu | Baigong Zheng | Chuanqiang Zhang | Zhongjun He | Hairong Liu | Xing Li | Hua Wu | Haifeng Wang
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

Simultaneous translation, which translates sentences before they are finished, is use- ful in many scenarios but is notoriously dif- ficult due to word-order differences. While the conventional seq-to-seq framework is only suitable for full-sentence translation, we pro- pose a novel prefix-to-prefix framework for si- multaneous translation that implicitly learns to anticipate in a single translation model. Within this framework, we present a very sim- ple yet surprisingly effective “wait-k” policy trained to generate the target sentence concur- rently with the source sentence, but always k words behind. Experiments show our strat- egy achieves low latency and reasonable qual- ity (compared to full-sentence translation) on 4 directions: zh↔en and de↔en.

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Robust Neural Machine Translation with Joint Textual and Phonetic Embedding
Hairong Liu | Mingbo Ma | Liang Huang | Hao Xiong | Zhongjun He
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

Neural machine translation (NMT) is notoriously sensitive to noises, but noises are almost inevitable in practice. One special kind of noise is the homophone noise, where words are replaced by other words with similar pronunciations. We propose to improve the robustness of NMT to homophone noises by 1) jointly embedding both textual and phonetic information of source sentences, and 2) augmenting the training dataset with homophone noises. Interestingly, to achieve better translation quality and more robustness, we found that most (though not all) weights should be put on the phonetic rather than textual information. Experiments show that our method not only significantly improves the robustness of NMT to homophone noises, but also surprisingly improves the translation quality on some clean test sets.

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Baidu Neural Machine Translation Systems for WMT19
Meng Sun | Bojian Jiang | Hao Xiong | Zhongjun He | Hua Wu | Haifeng Wang
Proceedings of the Fourth Conference on Machine Translation (Volume 2: Shared Task Papers, Day 1)

In this paper we introduce the systems Baidu submitted for the WMT19 shared task on Chinese<->English news translation. Our systems are based on the Transformer architecture with some effective improvements. Data selection, back translation, data augmentation, knowledge distillation, domain adaptation, model ensemble and re-ranking are employed and proven effective in our experiments. Our Chinese->English system achieved the highest case-sensitive BLEU score among all constrained submissions, and our English->Chinese system ranked the second in all submissions.

2012

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ICT:A System Combination for Chinese Semantic Dependency Parsing
Hao Xiong | Qun Liu
*SEM 2012: The First Joint Conference on Lexical and Computational Semantics – Volume 1: Proceedings of the main conference and the shared task, and Volume 2: Proceedings of the Sixth International Workshop on Semantic Evaluation (SemEval 2012)

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ICT: A Translation based Method for Cross-lingual Textual Entailment
Fandong Meng | Hao Xiong | Qun Liu
*SEM 2012: The First Joint Conference on Lexical and Computational Semantics – Volume 1: Proceedings of the main conference and the shared task, and Volume 2: Proceedings of the Sixth International Workshop on Semantic Evaluation (SemEval 2012)

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ICT: System Description for CoNLL-2012
Hao Xiong | Qun Liu
Joint Conference on EMNLP and CoNLL - Shared Task

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Discriminative Boosting from Dictionary and Raw Text – A Novel Approach to Build A Chinese Word Segmenter
Fandong Meng | Wenbin Jiang | Hao Xiong | Qun Liu
Proceedings of COLING 2012: Posters

2011

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ETS: An Error Tolerable System for Coreference Resolution
Hao Xiong | Linfeng Song | Fandong Meng | Yang Liu | Qun Liu | Yajuan Lv
Proceedings of the Fifteenth Conference on Computational Natural Language Learning: Shared Task

2009

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Sub-Sentence Division for Tree-Based Machine Translation
Hao Xiong | Wenwen Xu | Haitao Mi | Yang Liu | Qun Liu
Proceedings of the ACL-IJCNLP 2009 Conference Short Papers