Weakly Supervised Reasoning by Neuro-Symbolic Approaches

• URL: http://arxiv.org/abs/2309.13072v1
• Date: Tue, 19 Sep 2023 06:10:51 GMT
• Title: Weakly Supervised Reasoning by Neuro-Symbolic Approaches
• Authors: Xianggen Liu, Zhengdong Lu, Lili Mou
• Abstract summary: We will introduce our progress on neuro-symbolic approaches to NLP. We will design a neural system with symbolic latent structures for an NLP task. We will apply reinforcement learning or its relaxation to perform weakly supervised reasoning in the downstream task.
• Score: 28.98845133698169
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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