Zhanyi Liu


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An End-to-End Model for Question Answering over Knowledge Base with Cross-Attention Combining Global Knowledge
Yanchao Hao | Yuanzhe Zhang | Kang Liu | Shizhu He | Zhanyi Liu | Hua Wu | Jun Zhao
Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

With the rapid growth of knowledge bases (KBs) on the web, how to take full advantage of them becomes increasingly important. Question answering over knowledge base (KB-QA) is one of the promising approaches to access the substantial knowledge. Meanwhile, as the neural network-based (NN-based) methods develop, NN-based KB-QA has already achieved impressive results. However, previous work did not put more emphasis on question representation, and the question is converted into a fixed vector regardless of its candidate answers. This simple representation strategy is not easy to express the proper information in the question. Hence, we present an end-to-end neural network model to represent the questions and their corresponding scores dynamically according to the various candidate answer aspects via cross-attention mechanism. In addition, we leverage the global knowledge inside the underlying KB, aiming at integrating the rich KB information into the representation of the answers. As a result, it could alleviates the out-of-vocabulary (OOV) problem, which helps the cross-attention model to represent the question more precisely. The experimental results on WebQuestions demonstrate the effectiveness of the proposed approach.


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Active Learning for Dependency Parsing with Partial Annotation
Zhenghua Li | Min Zhang | Yue Zhang | Zhanyi Liu | Wenliang Chen | Hua Wu | Haifeng Wang
Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)


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Generalization of Words for Chinese Dependency Parsing
Xianchao Wu | Jie Zhou | Yu Sun | Zhanyi Liu | Dianhai Yu | Hua Wu | Haifeng Wang
Proceedings of the 13th International Conference on Parsing Technologies (IWPT 2013)


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Reordering with Source Language Collocations
Zhanyi Liu | Haifeng Wang | Hua Wu | Ting Liu | Sheng Li
Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies


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Improving Statistical Machine Translation with Monolingual Collocation
Zhanyi Liu | Haifeng Wang | Hua Wu | Sheng Li
Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics


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Collocation Extraction Using Monolingual Word Alignment Method
Zhanyi Liu | Haifeng Wang | Hua Wu | Sheng Li
Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing


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The TCH machine translation system for IWSLT 2008.
Haifeng Wang | Hua Wu | Xiaoguang Hu | Zhanyi Liu | Jianfeng Li | Dengjun Ren | Zhengyu Niu
Proceedings of the 5th International Workshop on Spoken Language Translation: Evaluation Campaign

This paper reports on the first participation of TCH (Toshiba (China) Research and Development Center) at the IWSLT evaluation campaign. We participated in all the 5 translation tasks with Chinese as source language or target language. For Chinese-English and English-Chinese translation, we used hybrid systems that combine rule-based machine translation (RBMT) method and statistical machine translation (SMT) method. For Chinese-Spanish translation, phrase-based SMT models were used. For the pivot task, we combined the translations generated by a pivot based statistical translation model and a statistical transfer translation model (firstly, translating from Chinese to English, and then from English to Spanish). Moreover, for better performance of MT, we improved each module in the MT systems as follows: adapting Chinese word segmentation to spoken language translation, selecting out-of-domain corpus to build language models, using bilingual dictionaries to correct word alignment results, handling NE translation and selecting translations from the outputs of multiple systems. According to the automatic evaluation results on the full test sets, we top in all the 5 tasks.


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Log-linear generation models for example-based machine translation
Zhanyi Liu | Hifeng Wang | Hua Wu
Proceedings of Machine Translation Summit XI: Papers


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Word Alignment for Languages with Scarce Resources Using Bilingual Corpora of Other Language Pairs
Haifeng Wang | Hua Wu | Zhanyi Liu
Proceedings of the COLING/ACL 2006 Main Conference Poster Sessions

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Boosting Statistical Word Alignment Using Labeled and Unlabeled Data
Hua Wu | Haifeng Wang | Zhanyi Liu
Proceedings of the COLING/ACL 2006 Main Conference Poster Sessions


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Alignment Model Adaptation for Domain-Specific Word Alignment
Hua Wu | Haifeng Wang | Zhanyi Liu
Proceedings of the 43rd Annual Meeting of the Association for Computational Linguistics (ACL’05)

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Example-based Machine Translation Based on TSC and Statistical Generation
Zhanyi Liu | Haifeng Wang | Hua Wu
Proceedings of Machine Translation Summit X: Papers

This paper proposes a novel Example-Based Machine Translation (EBMT) method based on Tree String Correspondence (TSC) and statistical generation. In this method, the translation examples are represented as TSC, which consists of three parts: a parse tree in the source language, a string in the target language, and the correspondences between the leaf nodes of the source language tree and the substrings of the target language string. During the translation, the input sentence is first parsed into a tree. Then the TSC forest is searched out if it is best matched with the parse tree. The translation is generated by using a statistical generation model to combine the target language strings in the TSCs. The generation model consists of three parts: the semantic similarity between words, the word translation probability, and the target language model. Based on the above method, we build an English-to-Chinese Machine Translation (ECMT) system. Experimental results indicate that the performance of our system is comparable with that of the state-of-the-art commercial ECMT systems.

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Improving Translation Memory with Word Alignment Information
Hua Wu | Haifeng Wang | Zhanyi Liu | Kai Tang
Proceedings of Machine Translation Summit X: Posters

This paper describes a generalized translation memory system, which takes advantage of sentence level matching, sub-sentential matching, and pattern-based machine translation technologies. All of the three techniques generate translation suggestions with the assistance of word alignment information. For the sentence level matching, the system generates the translation suggestion by modifying the translations of the most similar example with word alignment information. For sub-sentential matching, the system locates the translation fragments in several examples with word alignment information, and then generates the translation suggestion by combining these translation fragments. For pattern-based machine translation, the system first extracts translation patterns from examples using word alignment information and then generates translation suggestions with pattern matching. This system is compared with a traditional translation memory system without word alignment information in terms of translation efficiency and quality. Evaluation results indicate that our system improves the translation quality and saves about 20% translation time.