FAQ Retrieval Using Attentive Matching

The task of ranking question-answer pairs in response to an input query, aka FAQ (Frequently Asked Question) Retrieval, has traditionally been focused mostly on extracting relevance signals between query and questions based on extensive manual feature engineering. In this paper we propose multiple deep learning architectures designed for FAQ Retrieval that eliminate the need for feature engineering and are able to elegantly combine both query-question and query-answer similarities. We present experimental results showing that models that effectively combine both query-question and query-answer representations using attention mechanisms in a hierarchical manner yield the best results from all proposed models. We further verify the effectiveness of attention mechanisms for FAQ Retrieval by conducting experiments on a completely different attention-based architecture, originally designed for question duplicate detection tasks, and observing equally impressive experimental ranking results.