Xiaofeng Wu


2016

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ProphetMT: A Tree-based SMT-driven Controlled Language Authoring/Post-Editing Tool
Xiaofeng Wu | Jinhua Du | Qun Liu | Andy Way
Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16)

This paper presents ProphetMT, a tree-based SMT-driven Controlled Language (CL) authoring and post-editing tool. ProphetMT employs the source-side rules in a translation model and provides them as auto-suggestions to users. Accordingly, one might say that users are writing in a Controlled Language that is understood by the computer. ProphetMT also allows users to easily attach structural information as they compose content. When a specific rule is selected, a partial translation is promptly generated on-the-fly with the help of the structural information. Our experiments conducted on English-to-Chinese show that our proposed ProphetMT system can not only better regularise an author’s writing behaviour, but also significantly improve translation fluency which is vital to reduce the post-editing time. Additionally, when the writing and translation process is over, ProphetMT can provide an effective colour scheme to further improve the productivity of post-editors by explicitly featuring the relations between the source and target rules.

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Fast Gated Neural Domain Adaptation: Language Model as a Case Study
Jian Zhang | Xiaofeng Wu | Andy Way | Qun Liu
Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers

Neural network training has been shown to be advantageous in many natural language processing applications, such as language modelling or machine translation. In this paper, we describe in detail a novel domain adaptation mechanism in neural network training. Instead of learning and adapting the neural network on millions of training sentences – which can be very time-consuming or even infeasible in some cases – we design a domain adaptation gating mechanism which can be used in recurrent neural networks and quickly learn the out-of-domain knowledge directly from the word vector representations with little speed overhead. In our experiments, we use the recurrent neural network language model (LM) as a case study. We show that the neural LM perplexity can be reduced by 7.395 and 12.011 using the proposed domain adaptation mechanism on the Penn Treebank and News data, respectively. Furthermore, we show that using the domain-adapted neural LM to re-rank the statistical machine translation n-best list on the French-to-English language pair can significantly improve translation quality.

2015

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CASICT-DCU Participation in WMT2015 Metrics Task
Hui Yu | Qingsong Ma | Xiaofeng Wu | 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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DCU-Lingo24 Participation in WMT 2014 Hindi-English Translation task
Xiaofeng Wu | Rejwanul Haque | Tsuyoshi Okita | Piyush Arora | Andy Way | Qun Liu
Proceedings of the Ninth Workshop on Statistical Machine Translation

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RED, The DCU-CASICT Submission of Metrics Tasks
Xiaofeng Wu | Hui Yu | Qun Liu
Proceedings of the Ninth Workshop on Statistical Machine Translation

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RED: A Reference Dependency Based MT Evaluation Metric
Hui Yu | Xiaofeng Wu | Jun Xie | Wenbin Jiang | Qun Liu | Shouxun Lin
Proceedings of COLING 2014, the 25th International Conference on Computational Linguistics: Technical Papers

2013

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The CNGL-DCU-Prompsit Translation Systems for WMT13
Raphael Rubino | Antonio Toral | Santiago Cortés Vaíllo | Jun Xie | Xiaofeng Wu | Stephen Doherty | Qun Liu
Proceedings of the Eighth Workshop on Statistical Machine Translation

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DCU Participation in WMT2013 Metrics Task
Xiaofeng Wu | Hui Yu | Qun Liu
Proceedings of the Eighth Workshop on Statistical Machine Translation

2012

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System Combination with Extra Alignment Information
Xiaofeng Wu | Tsuyoshi Okita | Josef van Genabith | Qun Liu
Proceedings of the Second Workshop on Applying Machine Learning Techniques to Optimise the Division of Labour in Hybrid MT

2008

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A New Approach to Automatic Document Summarization
Xiaofeng Wu | Chengqing Zong
Proceedings of the Third International Joint Conference on Natural Language Processing: Volume-I