  Semantic text matching is a critical problem in information retrieval.
Recently, deep learning techniques have been widely used in this area and
obtained significant performance improvements. However, most models are black
boxes and it is hard to understand what happened in the matching process, due
to the poor interpretability of deep learning. This paper aims at tackling this
problem. The key idea is to test whether existing deep text matching methods
satisfy some fundamental heuristics in information retrieval. Specifically,
four heuristics are used in our study, i.e., term frequency constraint, term
discrimination constraint, length normalization constraints, and TF-length
constraint. Since deep matching models usually contain many parameters, it is
difficult to conduct a theoretical study for these complicated functions. In
this paper, We propose an empirical testing method. Specifically, We first
construct some queries and documents to make them satisfy the assumption in a
constraint, and then test to which extend a deep text matching model trained on
the original dataset satisfies the corresponding constraint. Besides, a famous
attribution based interpretation method, namely integrated gradient, is adopted
to conduct detailed analysis and guide for feasible improvement. Experimental
results on LETOR 4.0 and MS Marco show that all the investigated deep text
matching methods, both representation and interaction based methods, satisfy
the above constraints with high probabilities in statistics. We further extend
these constraints to the semantic settings, which are shown to be better
satisfied for all the deep text matching models. These empirical findings give
clear understandings on why deep text matching models usually perform well in
information retrieval. We believe the proposed evaluation methodology will be
useful for testing future deep text matching models.
