  Deep neural networks have been successfully applied to many text matching
tasks, such as paraphrase identification, question answering, and machine
translation. Although ad-hoc retrieval can also be formalized as a text
matching task, few deep models have been tested on it. In this paper, we study
a state-of-the-art deep matching model, namely MatchPyramid, on the ad-hoc
retrieval task. The MatchPyramid model employs a convolutional neural network
over the interactions between query and document to produce the matching score.
We conducted extensive experiments to study the impact of different pooling
sizes, interaction functions and kernel sizes on the retrieval performance.
Finally, we show that the MatchPyramid models can significantly outperform
several recently introduced deep matching models on the retrieval task, but
still cannot compete with the traditional retrieval models, such as BM25 and
language models.
