Query-bag Matching with Mutual Coverage for Information-seeking Conversations in E-commerce

TL;DR

This paper introduces a query-bag matching model for e-commerce conversations that leverages mutual coverage and fine-grained bag representations to improve matching accuracy and conversation quality.

Contribution

The paper proposes a novel query-bag matching approach utilizing mutual coverage and word-level bag representations, enhancing matching performance in e-commerce info-seeking conversations.

Findings

The model outperforms baseline methods on two datasets.

Mutual coverage effectively captures query-bag relevance.

Fine-grained bag representations improve matching accuracy.

Abstract

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.