  Text semantic matching is a fundamental task that has been widely used in
various scenarios, such as community question answering, information retrieval,
and recommendation. Most state-of-the-art matching models, e.g., BERT, directly
perform text comparison by processing each word uniformly. However, a query
sentence generally comprises content that calls for different levels of
matching granularity. Specifically, keywords represent factual information such
as action, entity, and event that should be strictly matched, while intents
convey abstract concepts and ideas that can be paraphrased into various
expressions. In this work, we propose a simple yet effective training strategy
for text semantic matching in a divide-and-conquer manner by disentangling
keywords from intents. Our approach can be easily combined with pre-trained
language models (PLM) without influencing their inference efficiency, achieving
stable performance improvements against a wide range of PLMs on three
benchmarks.
