As a key task of question answering, question retrieval has attracted much attention from the communities of academia and industry. Previous solutions mainly focus on the translation model, topic model, and deep learning techniques. Distinct from the previous solutions, we propose to construct fine-grained semantic representations of a question by a learned importance score assigned to each keyword, so that we can achieve a fine-grained question matching solution with these semantic representations of different lengths. Accordingly, we propose a multi-view semantic matching model by reusing the important keywords in multiple semantic representations. As a key of constructing fine-grained semantic representations, we are the first to use a cross-task weakly supervised extraction model that applies question-question labelled signals to supervise the keyword extraction process (i.e. to learn the keyword importance). The extraction model integrates the deep semantic representation and lexical matching information with statistical features to estimate the importance of keywords. We conduct extensive experiments on three public datasets and the experimental results show that our proposed model significantly outperforms the state-of-the-art solutions.

翻译：作为问答系统的关键任务，问题检索已引起学术界和工业界的广泛关注。现有解决方案主要基于翻译模型、主题模型和深度学习技术。与现有方法不同，我们提出通过为每个关键词分配学习得到的重要性分数来构建问题的细粒度语义表示，从而利用这些长度不同的语义表示实现细粒度的问题匹配方案。据此，我们提出一种多视角语义匹配模型，通过重复使用多个语义表示中的关键关键词实现匹配。作为构建细粒度语义表示的关键，我们首次采用跨任务弱监督抽取模型，该模型利用问题-问题配对标注信号监督关键词抽取过程（即学习关键词重要性）。该抽取模型融合深度语义表示、词汇匹配信息与统计特征来估计关键词重要性。我们在三个公开数据集上进行了大量实验，结果表明我们提出的模型显著优于现有最优解决方案。