The goal of answer selection is to select the most applicable answers from an answer candidate pool. It plays an essential role in numerous applications in information retrieval (IR) and natural language processing (NLP). In this paper, we introduce a novel Knowledge-enhanced Hierarchical Attention mechanism for Answer Selection (KHAAS), which fully exploits the common sense knowledge from knowledge bases (KBs) and input textual information. Specifically, we first devise a three-stage knowledge-enhanced hierarchical attention mechanism, including the word-level attention, the phrase-level attention, and the document-level attention to learn the fact-aware intra-document features within questions and answers by fusing the knowledge from both the question/answer and KB. Hence, we can leverage the semantic compositionality of the question/answer and learn more holistic knowledge-enhanced intra-document features of the question/answer at three levels of granularity. Second, after obtaining the knowledge-enhanced question and answer representations, we employ a multi-perspective co-attention network to learn the complex inter-document relationships between the question and answer representations from different representation subspaces, which can capture the interactive semantics of the question and answer representations at three levels. Finally, we propose an adaptive decision fusion method to learn a more effective and robust ensemble answer selection model by adaptively combining multiple classifiers learned with different levels of features. Experimental results on three large-scale answer selection datasets demonstrate that KHAAS consistently outperforms the compared methods.

选择答案的目的是从答案候选项池中选择最适用的答案。它在信息检索（IR）和自然语言处理（NLP）的众多应用中起着至关重要的作用。在本文中，我们介绍了一种新颖的知识选择应答机制（KHAAS），该机制充分利用了知识库（KB）中的常识知识并输入了文本信息。具体来说，我们首先设计一种三阶段的知识增强型分层注意力机制，包括单词级别的注意力，短语级别的注意力和文档级别的注意力，以通过以下方式学习问题和答案中的事实感知文档内特征：融合问题/答案和知识库中的知识。因此，我们可以利用问题/答案的语义组成来了解问题/答案在三个粒度级别上更全面的知识增强的文档内功能。其次，在获得知识增强的问题和答案表示之后，我们使用多角度的共同注意网络从不同的表示子空间中学习问题和答案表示之间的复杂文档间关系，从而可以捕获语言的交互语义。问答表示形式分为三个级别。最后，我们提出了一种自适应决策融合方法，通过将具有不同级别特征的多个分类器进行自适应组合，以学习更有效，更鲁棒的整体答案选择模型。