常识性问答的可推广神经符号系统

Neuro-Symbolic Artificial Intelligence Pub Date : 2021-12-22 DOI:10.3233/FAIA210360

A. Oltramari, Jonathan M Francis, Filip Ilievski, Kaixin Ma, Roshanak Mirzaee

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引用次数: 2

摘要

本章说明了适合语言理解的神经符号模型如何在下游任务中实现领域泛化和鲁棒性。讨论了神经语言模型与知识图集成的不同方法。描述了这种组合最适合的情况，包括对各种常识性问答基准数据集的定量评估和定性误差分析。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Generalizable Neuro-symbolic Systems for Commonsense Question Answering

This chapter illustrates how suitable neuro-symbolic models for language understanding can enable domain generalizability and robustness in downstream tasks. Different methods for integrating neural language models and knowledge graphs are discussed. The situations in which this combination is most appropriate are characterized, including quantitative evaluation and qualitative error analysis on a variety of commonsense question answering benchmark datasets.

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Neuro-Symbolic Artificial Intelligence

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