Abstract
Recent developments in language modeling have enabled large text encoders to derive a wealth of linguistic information from raw text corpora without supervision. Their success across natural language processing (NLP) tasks has called into question the role of man-made computational resources, such as verb lexicons, in supporting modern NLP. Still, probing analyses have concurrently exposed the limitations of the knowledge possessed by the large neural architectures, revealing them to be clever task solvers rather than self-taught linguists. Can human-designed lexical resources still help fill their knowledge gaps? Focusing on verb classification, we discuss approaches to generating verb classes multilingually and weigh the relative benefits of undertaking expensive lexicographic work and outsourcing the task to untrained native speakers. Then, we consider the evidence for the utility of augmenting pretrained language models with external verb knowledge and ponder the ways in which human expertise can continue to benefit multilingual NLP.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
