  Text matching is a core natural language processing research problem. How to
retain sufficient information on both content and structure information is one
important challenge. In this paper, we present a neural approach for
general-purpose text matching with deep mutual information estimation
incorporated. Our approach, Text matching with Deep Info Max (TIM), is
integrated with a procedure of unsupervised learning of representations by
maximizing the mutual information between text matching neural network's input
and output. We use both global and local mutual information to learn text
representations. We evaluate our text matching approach on several tasks
including natural language inference, paraphrase identification, and answer
selection. Compared to the state-of-the-art approaches, the experiments show
that our method integrated with mutual information estimation learns better
text representation and achieves better experimental results of text matching
tasks without exploiting pretraining on external data.
