FAQ Retrieval using Query-Question Similarity and BERT-Based Query-Answer Relevance
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Frequently Asked Question (FAQ) retrieval is an important task where the objective is to retrieve the appropriate Question-Answer (QA) pair from a database based on the user's query. In this study, we propose a FAQ retrieval system that considers the similarity between a user's query and a question computed by a traditional unsupervised information retrieval system, as well as the relevance between the query and an answer computed by the recently-proposed BERT model. By combining the rule-based approach and the flexible neural approach, the proposed system realizes robust FAQ retrieval. A common approach to FAQ retrieval is to construct labeled data for training, which takes a lot of costs. However, a FAQ database generally contains a too small number of QA pairs to train a model. To surmount this problem, we leverage FAQ sets that are similar to the one in question. We construct localgovFAQ dataset based on FAQ pages of administrative municipalities throughout Japan. In this research, we evaluate our approach on two datasets, localgovFAQ dataset and StackExchange dataset, and demonstrate that our proposed method works effectively.
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