  Frequently-Asked-Question (FAQ) retrieval provides an effective procedure for
responding to user's natural language based queries. Such platforms are
becoming common in enterprise chatbots, product question answering, and
preliminary technical support for customers. However, the challenge in such
scenarios lies in bridging the lexical and semantic gap between varied query
formulations and the corresponding answers, both of which typically have a very
short span. This paper proposes TI-S2S, a novel learning framework combining
TF-IDF based keyword extraction and Word2Vec embeddings for training a
Sequence-to-Sequence (Seq2Seq) architecture. It achieves high precision for FAQ
retrieval by better understanding the underlying intent of a user question
captured via the representative keywords. We further propose a variant with an
additional neural network module for guiding retrieval via relevant candidate
identification based on similarity features. Experiments on publicly available
dataset depict our approaches to provide around 92% precision-at-rank-5,
exhibiting nearly 13% improvement over existing approaches.
