![]() ![]() Understanding the intent behind a query offers several advantages:ġ) achieving better performance on query reformulation, query refinement, query prediction and query suggestion, andĢ) improving the document relevance modeling based on the search intent of the query. ![]() Query understanding is a key issue in information retrieval (IR), which aims to predict the search intent given a search query. We discuss the potential usage of such Q2ID technique through an example application. We demonstrate the effectiveness of our model by comparing with several state-of-the-art generation models on the Q2ID task. 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. 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. In this paper, therefore, we propose a novel Query-to-Intent-Description (Q2ID) task for query understanding. 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. 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.Īs 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. Many different tasks have been proposed for understanding users’ search queries, e.g., query classification or query clustering. Query understanding is a fundamental problem in information retrieval (IR), which has attracted continuous attention through the past decades. ![]()
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