A Generative-Symbolic Model for Logical Reasoning in NLUDownload PDF

Apr 21, 2021 (edited Jun 29, 2021)IJCAI 2021 Workshop NSNLI SubmissionReaders: Everyone
  • Keywords: neuro-symbolic, natural language understanding, semi-supervised learning, logical reasoning, generalization, robustness
  • TL;DR: A generative-symbolic model for logical reasoning in NLU
  • Abstract: Despite the recent success of language models (LMs) in natural language understanding (NLU), there are growing concerns about LMs' lack of logical reasoning abilities resulting in poor generalization and robustness. Faced with these concerns, neuro-symbolic integration may be a solution, which guides neural networks to understand the whole reasoning process by symbolic reasoners. In this paper, we propose a generative-symbolic model to explore how effectively this neuro-symbolic model benefits natural language understanding. The experiment on the CLUTRR dataset shows that this neural-symbolic model performs better than the corresponding neural model.
  • Track: Short paper
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