  In recent years, deep neural networks have led to exciting breakthroughs in
speech recognition, computer vision, and natural language processing (NLP)
tasks. However, there have been few positive results of deep models on ad-hoc
retrieval tasks. This is partially due to the fact that many important
characteristics of the ad-hoc retrieval task have not been well addressed in
deep models yet. Typically, the ad-hoc retrieval task is formalized as a
matching problem between two pieces of text in existing work using deep models,
and treated equivalent to many NLP tasks such as paraphrase identification,
question answering and automatic conversation. However, we argue that the
ad-hoc retrieval task is mainly about relevance matching while most NLP
matching tasks concern semantic matching, and there are some fundamental
differences between these two matching tasks. Successful relevance matching
requires proper handling of the exact matching signals, query term importance,
and diverse matching requirements. In this paper, we propose a novel deep
relevance matching model (DRMM) for ad-hoc retrieval. Specifically, our model
employs a joint deep architecture at the query term level for relevance
matching. By using matching histogram mapping, a feed forward matching network,
and a term gating network, we can effectively deal with the three relevance
matching factors mentioned above. Experimental results on two representative
benchmark collections show that our model can significantly outperform some
well-known retrieval models as well as state-of-the-art deep matching models.

Information Retrieval