Investigating a neural language model’s replicability of psycholinguistic experiments: A case study of NPI licensing - Shin, Unsub; Yi, Eunkyung; Song, Sanghoun (Frontiers in Psychology, 2023)
The recent success of deep-learning neural language models such as BERT has brought innovations to computational language research. The present study explores the possibility of using a language model in investigating human language processes, based on the case study of negative polarity items (NPIs). We first conducted an experiment with BERT to examine whether the model successfully captures the hierarchical structural relationship between an NPI and its licensor and also whether it may be led into an error, analogous to grammatical illusion shown in psycholinguistic experiment (Experiment 1). We also investigated whether the language model can capture the fine-grained semantic properties of NPI licensors and discriminates their subtle differences on the scale of licensing strengths (Experiment 2). The results of the two experiments suggest that overall the neural language model is highly sensitive to both syntactic and semantic constraints in NPI processing. The model's processing patterns and sensitivities are shown to be very close to humans,’ suggesting their role as a research tool or object in the study of language.