TRAVEL: Tag-Aware Conversational FAQ Retrieval via Reinforcement Learning

Yue Chen, Dingnan Jin, Chen Huang, Jia Liu, Wenqiang Lei

Submission Type: Regular Long Paper

Submission Track: Dialogue and Interactive Systems

Submission Track 2: Information Retrieval and Text Mining

Keywords: FAQ retrieval, Conversational FAQ Retrieval, Dialogue System, Human-machine interaction

Abstract:

Efficiently retrieving FAQ questions that match users' intent is essential for online customer service.
Existing methods aim to fully utilize the dynamic conversation context to enhance the semantic association between the user query and FAQ questions.
However, the conversation context contains noise, e.g., users may click questions they don't like, leading to inaccurate semantics modeling.
To tackle this, we introduce tags of FAQ questions, which can help us eliminate irrelevant information.
We later integrate them into a reinforcement learning framework and minimize the negative impact of irrelevant information in the dynamic conversation context.
We experimentally demonstrate our efficiency and effectiveness on conversational FAQ retrieval compared to other baselines.

Submission Number: 3621