A Hybrid Approach of Deep Semantic Matching and Deep Rank for Context Aware Question Answer System

A Hybrid Approach of Deep Semantic Matching and Deep Rank for Context Aware Question Answer System

Most of the existing Question Answer Systems focused on searching answers from the Knowledge-Base (KB), and ignore context aware information. Many Question Answer models perform well on public data-sets, but too complicated to be efficient in real world cases. Effectiveness, concurrency and system availability are equally important in industry which have large data and requests, we propose a Context Aware Question Answer System based on the Information Retrieval with Deep Semantic Matching and Deep Rank. It has been applied to the online question answer system for insurance Question Answer. By these means, we achieve both high QPS (Query Per Second) and effectiveness. Our approach improves the system’s ability to understand the question with context aware coreference resolution, subject completion, and the long sentence compression. After the matching questions are recalled from the ElasticSearch, Siamese CBOW (Continues Bag-Of-Words Model) and KBQA filter some unreasonable ones by entity alignment. After the result is sorted by the deep rank model with co-occurrence words and semantic features, our system does clarification or answer output. Finally, for those questions that we are unable to provide answers, a dialogue mining module as part of our Smart Knowledge-Base Platform is developed. This results in more than 10 times improvement in terms of efficiency for manpower involved in data labeling process.