Query Expansion with Neural Question-to-Answer Translation for FAQ-based Question Answering

We propose a novel Frequently Asked Question (FAQ) retrieval technique with a neural query expansion model. With the growth in Question Answering systems and mobile communications, FAQ retrieval systems have become widely used in site searches and call center support. However, FAQ retrieval often has lexical gaps between queries and answer documents. To bridge these gaps, we design a query expansion model on the basis of an Encoder-Decoder model as a type of deep neural network. The model learns the words that appear in answers for questions using Q&A pair documents and generates the expanded queries from inputted queries to retrieve answer documents. We evaluate our proposed technique in a multi-domain FAQ retrieval task. Experimental results show that our technique retrieves FAQs more accurately than the previous methods.