Recursive Neural Networks (RvNNs), which compose sequences according to their
underlying hierarchical syntactic structure, have performed well in several
natural language processing tasks compared to similar models without structural
biases. However, traditional RvNNs are incapable of inducing the latent
structure in a plain text sequence on their own. Several extensions have been
proposed to overcome this limitation. Nevertheless, these extensions tend to
rely on surrogate gradients or reinforcement learning at the cost of higher
bias or variance. In this work, we propose Continuous Recursive Neural Network
(CRvNN) as a backpropagation-friendly alternative to address the aforementioned
limitations. This is done by incorporating a continuous relaxation to the
induced structure. We demonstrate that CRvNN achieves strong performance in
challenging synthetic tasks such as logical inference and ListOps. We also show
that CRvNN performs comparably or better than prior latent structure models on
real-world tasks such as sentiment analysis and natural language inference.

本文提出 Continuous Recursive Neural Network (CRvNN) 作为递归神经网络（RvNN）的可替换方案，以解决传统 RvNN 在处理序列中诱导潜在结构方面的局限性，并通过在潜在结构中引入连续松弛来改进该方案，从而在逻辑推理和自然语言处理等多项任务中取得了显著表现。