Modelling Analogies and Analogical Reasoning: Connecting Cognitive Science Theory and NLP Research
This work provides a cross-disciplinary perspective to improve relational understanding in NLP, though it is incremental as it synthesizes existing theories without new empirical results.
The paper connects cognitive science theories of analogical reasoning to NLP research, showing how these processes can address challenges in relational understanding beyond entity-level similarity.
Analogical reasoning is an essential aspect of human cognition. In this paper, we summarize key theory about the processes underlying analogical reasoning from the cognitive science literature and relate it to current research in natural language processing. While these processes can be easily linked to concepts in NLP, they are generally not viewed through a cognitive lens. Furthermore, we show how these notions are relevant for several major challenges in NLP research, not directly related to analogy solving. This may guide researchers to better optimize relational understanding in text, as opposed to relying heavily on entity-level similarity.