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.

中文翻译：

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用深度互信息估计匹配文本

文本匹配是自然语言处理研究的核心问题。如何在内容和结构信息上保留足够的信息是一项重要挑战。在本文中，我们提出了一种包含深度互信息估计的通用文本匹配神经方法。我们的方法，文本匹配与 Deep Info Max (TIM)，通过最大化文本匹配神经网络输入和输出之间的互信息，与无监督的表征学习过程相结合。我们使用全局和局部互信息来学习文本表示。我们在几个任务上评估我们的文本匹配方法，包括自然语言推理、释义识别和答案选择。与最先进的方法相比，