  Nowadays e-commerce search has become an integral part of many people's
shopping routines. One critical challenge in today's e-commerce search is the
semantic matching problem where the relevant items may not contain the exact
terms in the user query. In this paper, we propose a novel deep neural network
based approach to query rewriting, in order to tackle this problem.
Specifically, we formulate query rewriting into a cyclic machine translation
problem to leverage abundant click log data. Then we introduce a novel cyclic
consistent training algorithm in conjunction with state-of-the-art machine
translation models to achieve the optimal performance in terms of query
rewriting accuracy. In order to make it practical in industrial scenarios, we
optimize the syntax tree construction to reduce computational cost and online
serving latency. Offline experiments show that the proposed method is able to
rewrite hard user queries into more standard queries that are more appropriate
for the inverted index to retrieve. Comparing with human curated rule-based
method, the proposed model significantly improves query rewriting diversity
while maintaining good relevancy. Online A/B experiments show that it improves
core e-commerce business metrics significantly. Since the summer of 2020, the
proposed model has been launched into our search engine production, serving
hundreds of millions of users.
