QUIDS: Query Intent Description for Exploratory Search via Dual Space Modeling

TL;DR

QUIDS introduces a dual-space contrastive learning approach to generate natural language intent descriptions, improving user understanding and search experience in exploratory search scenarios.

Contribution

The paper presents a novel dual-encoder and disentangling decoder framework with intent-driven hard negative sampling for intent description generation.

Findings

Outperforms state-of-the-art baselines on ROUGE and BERTScore

Achieves higher human and LLM evaluation scores

Effectively generates accurate intent descriptions

Abstract

In exploratory search, users often submit vague queries to investigate unfamiliar topics, but receive limited feedback about how the search engine understood their input. This leads to a self-reinforcing cycle of mismatched results and trial-and-error reformulation. To address this, we study the task of generating user-facing natural language query intent descriptions that surface what the system likely inferred the query to mean, based on post-retrieval evidence. We propose QUIDS, a method that leverages dual-space contrastive learning to isolate intent-relevant information while suppressing irrelevant content. QUIDS combines a dual-encoder representation space with a disentangling decoder that works together to produce concise and accurate intent descriptions. Enhanced by intent-driven hard negative sampling, the model significantly outperforms state-of-the-art baselines across ROUGE, BERTScore, and human/LLM evaluations. Our qualitative analysis confirms QUIDS' effectiveness in generating accurate intent descriptions for exploratory search. Our work contributes to improving the interaction between users and search engines by providing feedback to the user in exploratory search settings. Our code is available at https://github.com/menauwy/QUIDS