Query-bag Matching with Mutual Coverage for Information-seeking Conversations in E-commerce
This work addresses the specific challenge of enhancing conversation systems in e-commerce by improving query-bag matching, representing an incremental advancement in domain-specific text matching methods.
The paper tackles the problem of matching user queries to groups of related questions (bags) in e-commerce information-seeking conversations, proposing a model that uses mutual coverage and fine-grained bag representations to improve performance, with experiments on two datasets demonstrating its effectiveness.
Information-seeking conversation system aims at satisfying the information needs of users through conversations. Text matching between a user query and a pre-collected question is an important part of the information-seeking conversation in E-commerce. In the practical scenario, a sort of questions always correspond to a same answer. Naturally, these questions can form a bag. Learning the matching between user query and bag directly may improve the conversation performance, denoted as query-bag matching. Inspired by such opinion, we propose a query-bag matching model which mainly utilizes the mutual coverage between query and bag and measures the degree of the content in the query mentioned by the bag, and vice verse. In addition, the learned bag representation in word level helps find the main points of a bag in a fine grade and promotes the query-bag matching performance. Experiments on two datasets show the effectiveness of our model.