Keyword-Attentive Deep Semantic Matching
This work addresses the problem of diverse word tokens in query-question pairs for natural language processing applications like QA, but it is incremental as it builds upon BERT with keyword enhancements.
The authors tackled the challenge of measuring similarities between query-question pairs in open-domain scenarios by proposing a keyword-attentive approach to improve deep semantic matching, which outperformed existing baselines on a Chinese QA corpus.
Deep Semantic Matching is a crucial component in various natural language processing applications such as question and answering (QA), where an input query is compared to each candidate question in a QA corpus in terms of relevance. Measuring similarities between a query-question pair in an open domain scenario can be challenging due to diverse word tokens in the queryquestion pair. We propose a keyword-attentive approach to improve deep semantic matching. We first leverage domain tags from a large corpus to generate a domain-enhanced keyword dictionary. Built upon BERT, we stack a keyword-attentive transformer layer to highlight the importance of keywords in the query-question pair. During model training, we propose a new negative sampling approach based on keyword coverage between the input pair. We evaluate our approach on a Chinese QA corpus using various metrics, including precision of retrieval candidates and accuracy of semantic matching. Experiments show that our approach outperforms existing strong baselines. Our approach is general and can be applied to other text matching tasks with little adaptation.