【摘 要】
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Answer selection is a crucial subtask of the open domain question answering problem.In this paper,we introduce the Bi-directional Gated Memory Network(BGMN)to model the interactions between question a
【机 构】
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Key Laboratory of Computational Linguistics,Ministry of Education School of Electronics Engineering
【出 处】
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第十六届全国计算语言学学术会议暨第五届基于自然标注大数据的自然语言处理国际学术研讨会
论文部分内容阅读
Answer selection is a crucial subtask of the open domain question answering problem.In this paper,we introduce the Bi-directional Gated Memory Network(BGMN)to model the interactions between question and answer.We match question(P)and answer(Q)in two di-rections.In each direction(for example P →Q),sentence representation of P triggers an iterative attention process that aggregates informative evidence of Q.In each iteration,sentence representation of P and ag-gregated evidence of Q so far are passed through a gate determining the importance of the two when attend to every step of Q.Finally based on the aggregated evidence,the decision is made through a fully con-nected network.Experimental results on SemEval-2015 Task 3 dataset demonstrate that our proposed method substantially outperforms sev-eral strong baselines.Further experiments show that our model is general and can be applied to other sentence-pair modeling tasks.
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