Generalizable Neuro-symbolic Systems for Commonsense Question Answering

Abstract

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.

arXiv commentIn Pascal Hitzler, Md Kamruzzaman Sarker (eds.), Neuro-Symbolic Artificial Intelligence: The State of the Art. Frontiers in Artificial Intelligence and Applications Vol. 342, IOS Press, Amsterdam, 2022. arXiv admin note: text overlap with arXiv:2003.04707

References in corpus (6)

• ViLBERT: Pretraining Task-Agnostic Visiolinguistic Representations for Vision-and-Language Tasks
• CommonsenseQA: A Question Answering Challenge Targeting Commonsense Knowledge
• Option Comparison Network for Multiple-choice Reading Comprehension
• Machine Common Sense Concept Paper
• Neuro-symbolic Architectures for Context Understanding
• Commonsense Knowledge in Wikidata