Generalizable Neuro-symbolic Systems for Commonsense Question Answering
This work addresses the problem of enhancing AI systems' commonsense reasoning for researchers and practitioners, but it appears incremental as it discusses existing methods.
The paper explores neuro-symbolic models combining neural language models and knowledge graphs to improve domain generalizability and robustness in commonsense question answering, with evaluations on benchmark datasets.
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.