  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.
