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arXiv · 1811.02959

Compositional Language Understanding with Text-based Relational Reasoning

Koustuv Sinha·Shagun Sodhani·William L. Hamilton·Joelle Pineau
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Abstract

Neural networks for natural language reasoning have largely focused on extractive, fact-based question-answering (QA) and common-sense inference. However, it is also crucial to understand the extent to which neural networks can perform relational reasoning and combinatorial generalization from natural language---abilities that are often obscured by annotation artifacts and the dominance of language modeling in standard QA benchmarks. In this work, we present a novel benchmark dataset for language understanding that isolates performance on relational reasoning. We also present a neural message-passing baseline and show that this model, which incorporates a relational inductive bias, is superior at combinatorial generalization compared to a traditional recurrent neural network approach.

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Koustuv Sinha, Shagun Sodhani, William L. Hamilton, Joelle Pineau. 2018-11-08. Compositional Language Understanding with Text-based Relational Reasoning. https://arxiv.org/abs/1811.02959

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