A Closer Look at the Performance of Neural Language Models on Reflexive Anaphor Licensing
Year: 2020
Type: article
Abstract: An emerging line of work uses psycholinguistic methods to evaluate the syntactic generalizations acquired by neural language models (NLMs). While this approach has shown NLMs to be capable of learning a wide range of linguistic knowledge, confounds in the design of previous experiments may have obscured the potential of NLMs to learn certain grammatical phenomena. Here we re-evaluate the performance of a range of NLMs on reflexive anaphor licensing. Under our paradigm, the models consistently show stronger evidence of learning than reported in previous work. Our approach demonstrates the value of well-controlled psycholinguistic methods in gaining a fine-grained understanding of NLM learning potential.
DOI: 10.7275/67qw-mf84
Authors: Hu, Jennifer, Chen, Sherry Y., Levy, Roger P.
Institution: Massachusetts Institute of Technology
Language: English
Subfield: Language and Linguistics
Field: Arts and Humanities
Domain: Social Sciences
Open Access status: green
OpenAlex ID: W2994726368