  One approach to matching texts from asymmetrical domains is projecting the
input sequences into a common semantic space as feature vectors upon which the
matching function can be readily defined and learned. In real-world matching
practices, it is often observed that with the training goes on, the feature
vectors projected from different domains tend to be indistinguishable. The
phenomenon, however, is often overlooked in existing matching models. As a
result, the feature vectors are constructed without any regularization, which
inevitably increases the difficulty of learning the downstream matching
functions. In this paper, we propose a novel match method tailored for text
matching in asymmetrical domains, called WD-Match. In WD-Match, a Wasserstein
distance-based regularizer is defined to regularize the features vectors
projected from different domains. As a result, the method enforces the feature
projection function to generate vectors such that those correspond to different
domains cannot be easily discriminated. The training process of WD-Match
amounts to a game that minimizes the matching loss regularized by the
Wasserstein distance. WD-Match can be used to improve different text matching
methods, by using the method as its underlying matching model. Four popular
text matching methods have been exploited in the paper. Experimental results
based on four publicly available benchmarks showed that WD-Match consistently
outperformed the underlying methods and the baselines.
