Deep learning has largely improved the performance of various natural
language processing (NLP) tasks. However, most deep learning models are
black-box machinery, and lack explicit interpretation. In this chapter, we will
introduce our recent progress on neuro-symbolic approaches to NLP, which
combines different schools of AI, namely, symbolism and connectionism.
Generally, we will design a neural system with symbolic latent structures for
an NLP task, and apply reinforcement learning or its relaxation to perform
weakly supervised reasoning in the downstream task. Our framework has been
successfully applied to various tasks, including table query reasoning,
syntactic structure reasoning, information extraction reasoning, and rule
reasoning. For each application, we will introduce the background, our
approach, and experimental results.

深度学习在自然语言处理 (NLP) 任务中取得了显著进展，本研究提出了一种神经符号学习方法，将符号主义和连接主义两种人工智能学派相结合，通过强化学习或其弱化版本在下游任务中实现具有符号潜在结构的神经系统的弱监督推理。该框架已成功应用于表格查询推理、句法结构推理、信息抽取推理和规则推理等各种任务。