论文标题
使用细粒语义表示的问题检索的多视图语义匹配
Multi-view Semantic Matching of Question retrieval using Fine-grained Semantic Representations
论文作者
论文摘要
作为回答问题的关键任务，问题检索吸引了学术界和行业社区的广泛关注。以前的解决方案主要关注翻译模型，主题模型和深度学习技术。与以前的解决方案不同，我们建议通过分配给每个关键字的学习重要性得分来构建问题的细粒语义表示，以便我们可以通过不同长度的这些语义表示来实现精细粒度的问题匹配解决方案。因此，我们通过在多个语义表示中重复使用重要的关键字来提出一个多视图语义匹配模型。 作为构建细颗粒语义表示的关键，我们是第一个使用交叉任务弱监督的提取模型，该模型应用了标有问题的标记信号来监督关键字提取过程（即学习关键字值）。提取模型将深层语义表示和词汇匹配信息与统计特征集成在一起，以估计关键字的重要性。我们对三个公共数据集进行了广泛的实验，实验结果表明，我们提出的模型大大优于最先进的解决方案。
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.
