Junwei Bao


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Learning to Decouple Relations: Few-Shot Relation Classification with Entity-Guided Attention and Confusion-Aware Training
Yingyao Wang | Junwei Bao | Guangyi Liu | Youzheng Wu | Xiaodong He | Bowen Zhou | Tiejun Zhao
Proceedings of the 28th International Conference on Computational Linguistics

This paper aims to enhance the few-shot relation classification especially for sentences that jointly describe multiple relations. Due to the fact that some relations usually keep high co-occurrence in the same context, previous few-shot relation classifiers struggle to distinguish them with few annotated instances. To alleviate the above relation confusion problem, we propose CTEG, a model equipped with two novel mechanisms to learn to decouple these easily-confused relations. On the one hand, an Entity -Guided Attention (EGA) mechanism, which leverages the syntactic relations and relative positions between each word and the specified entity pair, is introduced to guide the attention to filter out information causing confusion. On the other hand, a Confusion-Aware Training (CAT) method is proposed to explicitly learn to distinguish relations by playing a pushing-away game between classifying a sentence into a true relation and its confusing relation. Extensive experiments are conducted on the FewRel dataset, and the results show that our proposed model achieves comparable and even much better results to strong baselines in terms of accuracy. Furthermore, the ablation test and case study verify the effectiveness of our proposed EGA and CAT, especially in addressing the relation confusion problem.


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Constraint-Based Question Answering with Knowledge Graph
Junwei Bao | Nan Duan | Zhao Yan | Ming Zhou | Tiejun Zhao
Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers

WebQuestions and SimpleQuestions are two benchmark data-sets commonly used in recent knowledge-based question answering (KBQA) work. Most questions in them are ‘simple’ questions which can be answered based on a single relation in the knowledge base. Such data-sets lack the capability of evaluating KBQA systems on complicated questions. Motivated by this issue, we release a new data-set, namely ComplexQuestions, aiming to measure the quality of KBQA systems on ‘multi-constraint’ questions which require multiple knowledge base relations to get the answer. Beside, we propose a novel systematic KBQA approach to solve multi-constraint questions. Compared to state-of-the-art methods, our approach not only obtains comparable results on the two existing benchmark data-sets, but also achieves significant improvements on the ComplexQuestions.

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DocChat: An Information Retrieval Approach for Chatbot Engines Using Unstructured Documents
Zhao Yan | Nan Duan | Junwei Bao | Peng Chen | Ming Zhou | Zhoujun Li | Jianshe Zhou
Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)


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Knowledge-Based Question Answering as Machine Translation
Junwei Bao | Nan Duan | Ming Zhou | Tiejun Zhao
Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)