  In real-world question-answering (QA) systems, ill-formed questions, such as
wrong words, ill word order, and noisy expressions, are common and may prevent
the QA systems from understanding and answering them accurately. In order to
eliminate the effect of ill-formed questions, we approach the question
refinement task and propose a unified model, QREFINE, to refine the ill-formed
questions to well-formed question. The basic idea is to learn a Seq2Seq model
to generate a new question from the original one. To improve the quality and
retrieval performance of the generated questions, we make two major
improvements: 1) To better encode the semantics of ill-formed questions, we
enrich the representation of questions with character embedding and the recent
proposed contextual word embedding such as BERT, besides the traditional
context-free word embeddings; 2) To make it capable to generate desired
questions, we train the model with deep reinforcement learning techniques that
considers an appropriate wording of the generation as an immediate reward and
the correlation between generated question and answer as time-delayed long-term
rewards. Experimental results on real-world datasets show that the proposed
QREFINE method can generate refined questions with more readability but fewer
mistakes than the original questions provided by users. Moreover, the refined
questions also significantly improve the accuracy of answer retrieval.
