  Frequently asked question (FAQ) retrieval, with the purpose of providing
information on frequent questions or concerns, has far-reaching applications in
many areas, where a collection of question-answer (Q-A) pairs compiled a priori
can be employed to retrieve an appropriate answer in response to a user\u2019s
query that is likely to reoccur frequently. To this end, predominant approaches
to FAQ retrieval typically rank question-answer pairs by considering either the
similarity between the query and a question (q-Q), the relevance between the
query and the associated answer of a question (q-A), or combining the clues
gathered from the q-Q similarity measure and the q-A relevance measure. In this
paper, we extend this line of research by combining the clues gathered from the
q-Q similarity measure and the q-A relevance measure and meanwhile injecting
extra word interaction information, distilled from a generic (open domain)
knowledge base, into a contextual language model for inferring the q-A
relevance. Furthermore, we also explore to capitalize on domain-specific
topically-relevant relations between words in an unsupervised manner, acting as
a surrogate to the supervised domain-specific knowledge base information. As
such, it enables the model to equip sentence representations with the knowledge
about domain-specific and topically-relevant relations among words, thereby
providing a better q-A relevance measure. We evaluate variants of our approach
on a publicly-available Chinese FAQ dataset, and further apply and
contextualize it to a large-scale question-matching task, which aims to search
questions from a QA dataset that have a similar intent as an input query.
Extensive experimental results on these two datasets confirm the promising
performance of the proposed approach in relation to some state-of-the-art ones.
