QUIDS: Query Intent Generation via Dual Space Modeling

2024-10-16

arXiv.org

AI Summary

To address the challenges of ambiguous user queries and insufficient intent feedback in exploratory search—leading to iterative trial-and-error—we propose, for the first time, the generative query intent description task. Our method jointly models relevant and irrelevant documents to automatically generate precise, interpretable intent descriptions. Technically, we introduce a novel dual-space modeling paradigm: semantic separation of relevant and irrelevant documents in a representation space, and explicit suppression of irrelevant information in a disentangled space. We design a semantic projection encoder, a semantic disentanglement decoder, and an attention-guided generation framework. Experiments on benchmark datasets demonstrate that our approach significantly outperforms existing intent classification, clustering, and query summarization methods. Attention visualization confirms its effectiveness in filtering out distracting topics, yielding more accurate and interpretable intent descriptions.

Technology Category

• Natural Language Processing: Generation
• Data Mining & Knowledge Management: Intelligent Query Processing
• Search and Optimization: Learning to Search

Application Category

• Search and Retrieval-Augmented AI: Web query analysis, representation and understanding
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• User Modeling, Personalization and Recommendation: Explainable and interpretable methods for personalization

Abstract

Query understanding is a crucial component of Information Retrieval (IR), aimed at identifying the underlying search intent of textual queries. However, most existing approaches oversimplify this task into query classification or clustering, which fails to fully capture the nuanced intent behind the query. In this paper, we address the task of query intent generation: to automatically generate detailed and precise intent descriptions for search queries using relevant and irrelevant documents given a query. These intent descriptions can help users understand why the search engine considered the top-ranked documents relevant, and provide more transparency to the retrieval process. We propose a dual-space model that uses semantic relevance and irrelevance information in the returned documents to explain the understanding of the query intent. Specifically, in the encoding process, we project, separate, and distinguish relevant and irrelevant documents in the representation space. Then, we introduce a semantic decoupling model in the novel disentangling space, where the semantics of irrelevant information are removed from the relevant space, ensuring that only the essential and relevant intent is captured. This process refines the understanding of the query and provides more accurate explanations for the search results. Experiments on benchmark data demonstrate that our methods produce high-quality query intent descriptions, outperforming existing methods for this task, as well as state-of-the-art query-based summarization methods. A token-level visualization of attention scores reveals that our model effectively reduces the focus on irrelevant intent topics. Our findings open up promising research and application directions for query intent generation, particularly in exploratory search.

Problem

• Generates natural language descriptions of search query intent
• Addresses vague queries in exploratory search through dual-space modeling
• Improves user-system interaction by providing interpretable search feedback

Innovation

• Dual-space contrastive learning isolates intent information
• Dual-encoder representation with disentangling decoder produces descriptions
• Intent-driven hard negative sampling enhances model performance