Generative Question Refinement with Deep Reinforcement Learning in Retrieval-based QA System
Generative Question Refinement with Deep Reinforcement Learning in Retrieval-based QA System
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DOI：
10.1145/3357384.3358046
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发表时间：
2019-08
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影响因子：
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通讯作者：
Ye Liu;Chenwei Zhang;Xiaohui Yan;Yi Chang;Philip S. Yu
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作者：
Ye Liu;Chenwei Zhang;Xiaohui Yan;Yi Chang;Philip S. Yu
在现实世界的问答系统中，诸如错误的单词、不良的词序和嘈杂的表达之类的不良形式的问题是常见的，并且可能妨碍问答系统准确地理解和回答它们。为了消除病态问题的影响，我们对问题精化任务进行了研究，提出了一个统一的模型QREFINE，将病态问题精化为良构问题。基本思想是学习Seq 2Seq模型，从原始问题生成新问题。为了提高生成问题的质量和检索性能，我们做了两个主要的改进：1）为了更好地对病态问题的语义进行编码，除了传统的上下文无关词嵌入外，我们还使用字符嵌入和最近提出的上下文词嵌入（如BERT）来丰富问题的表示; 2）为了使其能够生成期望的问题，我们使用深度强化学习技术训练模型，该技术将生成的适当措辞视为即时奖励，并将生成的问题和答案之间的相关性视为时间延迟长，长期奖励在真实数据集上的实验结果表明，与用户提供的原始问题相比，所提QREFINE方法能够生成可读性更强、错误更少的精炼问题。此外，精炼的问题也显着提高答案检索的准确率。
In real-world question-answering (QA) systems, ill-formed questions, such as wrong words, ill word order and noisy expressions, are common and may prevent the QA systems from understanding and answering them accurately. In order to eliminate the effect of ill-formed questions, we approach the question refinement task and propose a unified model, QREFINE, to refine the ill-formed questions to well-formed question. The basic idea is to learn a Seq2Seq model to generate a new question from the original one. To improve the quality and retrieval performance of the generated questions, we make two major improvements: 1) To better encode the semantics of ill-formed questions, we enrich the representation of questions with character embedding and the recent proposed contextual word embedding such as BERT, besides the traditional context-free word embeddings; 2) To make it capable to generate desired questions, we train the model with deep reinforcement learning techniques that considers an appropriate wording of the generation as an immediate reward and the correlation between generated question and answer as time-delayed long-term rewards. Experimental results on real-world datasets show that the proposed QREFINE method can generate refined questions with more readability but fewer mistakes than the original questions provided by users. Moreover, the refined questions also significantly improve the accuracy of answer retrieval.