Liangyou Li


2019

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Huawei’s NMT Systems for the WMT 2019 Biomedical Translation Task
Wei Peng | Jianfeng Liu | Liangyou Li | Qun Liu
Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2)

This paper describes Huawei’s neural machine translation systems for the WMT 2019 biomedical translation shared task. We trained and fine-tuned our systems on a combination of out-of-domain and in-domain parallel corpora for six translation directions covering English–Chinese, English–French and English–German language pairs. Our submitted systems achieve the best BLEU scores on English–French and English–German language pairs according to the official evaluation results. In the English–Chinese translation task, our systems are in the second place. The enhanced performance is attributed to more in-domain training and more sophisticated models developed. Development of translation models and transfer learning (or domain adaptation) methods has significantly contributed to the progress of the task.

2017

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Context-Aware Graph Segmentation for Graph-Based Translation
Liangyou Li | Andy Way | Qun Liu
Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 2, Short Papers

In this paper, we present an improved graph-based translation model which segments an input graph into node-induced subgraphs by taking source context into consideration. Translations are generated by combining subgraph translations left-to-right using beam search. Experiments on Chinese–English and German–English demonstrate that the context-aware segmentation significantly improves the baseline graph-based model.

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Semantics-Enhanced Task-Oriented Dialogue Translation: A Case Study on Hotel Booking
Longyue Wang | Jinhua Du | Liangyou Li | Zhaopeng Tu | Andy Way | Qun Liu
Proceedings of the IJCNLP 2017, System Demonstrations

We showcase TODAY, a semantics-enhanced task-oriented dialogue translation system, whose novelties are: (i) task-oriented named entity (NE) definition and a hybrid strategy for NE recognition and translation; and (ii) a novel grounded semantic method for dialogue understanding and task-order management. TODAY is a case-study demo which can efficiently and accurately assist customers and agents in different languages to reach an agreement in a dialogue for the hotel booking.

2016

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Topic-Informed Neural Machine Translation
Jian Zhang | Liangyou Li | Andy Way | Qun Liu
Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers

In recent years, neural machine translation (NMT) has demonstrated state-of-the-art machine translation (MT) performance. It is a new approach to MT, which tries to learn a set of parameters to maximize the conditional probability of target sentences given source sentences. In this paper, we present a novel approach to improve the translation performance in NMT by conveying topic knowledge during translation. The proposed topic-informed NMT can increase the likelihood of selecting words from the same topic and domain for translation. Experimentally, we demonstrate that topic-informed NMT can achieve a 1.15 (3.3% relative) and 1.67 (5.4% relative) absolute improvement in BLEU score on the Chinese-to-English language pair using NIST 2004 and 2005 test sets, respectively, compared to NMT without topic information.

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Graph-Based Translation Via Graph Segmentation
Liangyou Li | Andy Way | Qun Liu
Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

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Phrase-Level Combination of SMT and TM Using Constrained Word Lattice
Liangyou Li | Andy Way | Qun Liu
Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

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Extending Phrase-Based Translation with Dependencies by Using Graphs
Liangyou Li | Andy Way | Qun Liu
Proceedings of the 2nd Workshop on Semantics-Driven Machine Translation (SedMT 2016)

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Combining Translation Memories and Syntax-Based SMT: Experiments with Real Industrial Data
Liangyou Li | Carla Parra Escartin | Qun Liu
Proceedings of the 19th Annual Conference of the European Association for Machine Translation

2015

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Dependency Graph-to-String Translation
Liangyou Li | Andy Way | Qun Liu
Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing

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MT Tuning on RED: A Dependency-Based Evaluation Metric
Liangyou Li | Hui Yu | Qun Liu
Proceedings of the Tenth Workshop on Statistical Machine Translation

2014

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The DCU-ICTCAS MT system at WMT 2014 on German-English Translation Task
Liangyou Li | Xiaofeng Wu | Santiago Cortés Vaíllo | Jun Xie | Andy Way | Qun Liu
Proceedings of the Ninth Workshop on Statistical Machine Translation

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Transformation and Decomposition for Efficiently Implementing and Improving Dependency-to-String Model In Moses
Liangyou Li | Jun Xie | Andy Way | Qun Liu
Proceedings of SSST-8, Eighth Workshop on Syntax, Semantics and Structure in Statistical Translation

2012

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Phrase-Based Evaluation for Machine Translation
Liangyou Li | Zhengxian Gong | Guodong Zhou
Proceedings of COLING 2012: Posters