Construction Grammar Provides Unique Insight into Neural Language Models
It provides a critical review for researchers in computational linguistics and NLP, but is incremental as it synthesizes existing work rather than presenting new empirical results.
This position paper analyzes how Construction Grammar (CxG) has been used to probe large pretrained language models, identifying methodological gaps and proposing future research directions to address challenges in this emerging field.
Construction Grammar (CxG) has recently been used as the basis for probing studies that have investigated the performance of large pretrained language models (PLMs) with respect to the structure and meaning of constructions. In this position paper, we make suggestions for the continuation and augmentation of this line of research. We look at probing methodology that was not designed with CxG in mind, as well as probing methodology that was designed for specific constructions. We analyse selected previous work in detail, and provide our view of the most important challenges and research questions that this promising new field faces.