Probing for Understanding of English Verb Classes and Alternations in Large Pre-trained Language Models

• URL: http://arxiv.org/abs/2209.04811v1
• Date: Sun, 11 Sep 2022 08:04:40 GMT
• Title: Probing for Understanding of English Verb Classes and Alternations in Large Pre-trained Language Models
• Authors: David K. Yi, James V. Bruno, Jiayu Han, Peter Zukerman, Shane Steinert-Threlkeld
• Abstract summary: We investigate the extent to which verb alternation classes are encoded in the embeddings of Large Pre-trained Language Models. We find that contextual embeddings from PLMs achieve astonishingly high accuracies on tasks across most classes.
• Score: 4.243426191555036
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We investigate the extent to which verb alternation classes, as described by Levin (1993), are encoded in the embeddings of Large Pre-trained Language Models (PLMs) such as BERT, RoBERTa, ELECTRA, and DeBERTa using selectively constructed diagnostic classifiers for word and sentence-level prediction tasks. We follow and expand upon the experiments of Kann et al. (2019), which aim to probe whether static embeddings encode frame-selectional properties of verbs. At both the word and sentence level, we find that contextual embeddings from PLMs not only outperform non-contextual embeddings, but achieve astonishingly high accuracies on tasks across most alternation classes. Additionally, we find evidence that the middle-to-upper layers of PLMs achieve better performance on average than the lower layers across all probing tasks.

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