Empirical Study of Multi-level Convolution Models for IR Based on Representations and Interactions

Yifan Nie, Yanling Li, Jian-Yun Nie

Abstract: Deep learning models have been employed to perform IR tasks and have shown competitive results. Depending on the structure of the models, previous deep IR models could be roughly divided into: representation-based models and interaction-based models. A number of experiments have been conducted to test these models, but often under different conditions, making it difficult to draw a clear conclusion on their comparison. In order to compare the two learning schemas for ad hoc search under the same condition, we build similar convolution networks to learn either representations or interaction patterns between document and query and test them on the same test collection. In addition, we also propose multi- level matching models to cope with various types of query, rather than the existing single-level matching. Our experiments show that interaction-based approach generally performs better than representation-based approach, and multi-level matching performs better than single-level matching. We will provide some possible explanations to these observations.

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