Query Understanding via Intent Description Generation

TL;DR

This paper introduces the Q2ID task for query understanding, aiming to generate detailed intent descriptions from relevant and irrelevant documents, and proposes a contrastive generation model called CtrsGen to improve this process.

Contribution

The paper proposes a novel Q2ID task and a contrastive generation model, CtrsGen, to enhance query understanding by generating precise intent descriptions from document sets.

Findings

CtrsGen outperforms state-of-the-art models on Q2ID task

Generated descriptions are more detailed and accurate

Demonstrates potential for improved IR systems

Abstract

Query understanding is a fundamental problem in information retrieval (IR), which has attracted continuous attention through the past decades. Many different tasks have been proposed for understanding users' search queries, e.g., query classification or query clustering. However, it is not that precise to understand a search query at the intent class/cluster level due to the loss of many detailed information. As we may find in many benchmark datasets, e.g., TREC and SemEval, queries are often associated with a detailed description provided by human annotators which clearly describes its intent to help evaluate the relevance of the documents. If a system could automatically generate a detailed and precise intent description for a search query, like human annotators, that would indicate much better query understanding has been achieved. In this paper, therefore, we propose a novel Query-to-Intent-Description (Q2ID) task for query understanding. Unlike those existing ranking tasks which leverage the query and its description to compute the relevance of documents, Q2ID is a reverse task which aims to generate a natural language intent description based on both relevant and irrelevant documents of a given query. To address this new task, we propose a novel Contrastive Generation model, namely CtrsGen for short, to generate the intent description by contrasting the relevant documents with the irrelevant documents given a query. We demonstrate the effectiveness of our model by comparing with several state-of-the-art generation models on the Q2ID task. We discuss the potential usage of such Q2ID technique through an example application.