Chen Liang


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Multi-Domain Neural Machine Translation with Word-Level Adaptive Layer-wise Domain Mixing
Haoming Jiang | Chen Liang | Chong Wang | Tuo Zhao
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

Many multi-domain neural machine translation (NMT) models achieve knowledge transfer by enforcing one encoder to learn shared embedding across domains. However, this design lacks adaptation to individual domains. To overcome this limitation, we propose a novel multi-domain NMT model using individual modules for each domain, on which we apply word-level, adaptive and layer-wise domain mixing. We first observe that words in a sentence are often related to multiple domains. Hence, we assume each word has a domain proportion, which indicates its domain preference. Then word representations are obtained by mixing their embedding in individual domains based on their domain proportions. We show this can be achieved by carefully designing multi-head dot-product attention modules for different domains, and eventually taking weighted averages of their parameters by word-level layer-wise domain proportions. Through this, we can achieve effective domain knowledge sharing and capture fine-grained domain-specific knowledge as well. Our experiments show that our proposed model outperforms existing ones in several NMT tasks.


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Distractor Generation for Multiple Choice Questions Using Learning to Rank
Chen Liang | Xiao Yang | Neisarg Dave | Drew Wham | Bart Pursel | C. Lee Giles
Proceedings of the Thirteenth Workshop on Innovative Use of NLP for Building Educational Applications

We investigate how machine learning models, specifically ranking models, can be used to select useful distractors for multiple choice questions. Our proposed models can learn to select distractors that resemble those in actual exam questions, which is different from most existing unsupervised ontology-based and similarity-based methods. We empirically study feature-based and neural net (NN) based ranking models with experiments on the recently released SciQ dataset and our MCQL dataset. Experimental results show that feature-based ensemble learning methods (random forest and LambdaMART) outperform both the NN-based method and unsupervised baselines. These two datasets can also be used as benchmarks for distractor generation.

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Research on Entity Relation Extraction for Military Field
Chen Liang | Hongying Zan | Yajun Liu | Yunfang Wu
Proceedings of the 32nd Pacific Asia Conference on Language, Information and Computation


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Neural Symbolic Machines: Learning Semantic Parsers on Freebase with Weak Supervision
Chen Liang | Jonathan Berant | Quoc Le | Kenneth D. Forbus | Ni Lao
Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Harnessing the statistical power of neural networks to perform language understanding and symbolic reasoning is difficult, when it requires executing efficient discrete operations against a large knowledge-base. In this work, we introduce a Neural Symbolic Machine, which contains (a) a neural “programmer”, i.e., a sequence-to-sequence model that maps language utterances to programs and utilizes a key-variable memory to handle compositionality (b) a symbolic “computer”, i.e., a Lisp interpreter that performs program execution, and helps find good programs by pruning the search space. We apply REINFORCE to directly optimize the task reward of this structured prediction problem. To train with weak supervision and improve the stability of REINFORCE, we augment it with an iterative maximum-likelihood training process. NSM outperforms the state-of-the-art on the WebQuestionsSP dataset when trained from question-answer pairs only, without requiring any feature engineering or domain-specific knowledge.


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Measuring Prerequisite Relations Among Concepts
Chen Liang | Zhaohui Wu | Wenyi Huang | C. Lee Giles
Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing

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Storybase: Towards Building a Knowledge Base for News Events
Zhaohui Wu | Chen Liang | C. Lee Giles
Proceedings of ACL-IJCNLP 2015 System Demonstrations


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Topical Word Trigger Model for Keyphrase Extraction
Zhiyuan Liu | Chen Liang | Maosong Sun
Proceedings of COLING 2012

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Expert Finding for Microblog Misinformation Identification
Chen Liang | Zhiyuan Liu | Maosong Sun
Proceedings of COLING 2012: Posters