Abstraction not Memory: BERT and the English Article System

Harish Tayyar Madabushi, Dagmar Divjak, Petar Milin

Research output: Chapter in Book/Report/Conference proceedingChapter in a published conference proceeding

Abstract

Article prediction is a task that has long defied accurate linguistic description. As such, this task is ideally suited to evaluate models on their ability to emulate native-speaker intuition. To this end, we compare the performance of native English speakers and pre-trained models on the task of article prediction set up as a three way choice (a/an, the, zero). Our experiments with BERT show that BERT outperforms humans on this task across all articles. In particular, BERT is far superior to humans at detecting the zero article, possibly because we insert them using rules that the deep neural model can easily pick up. More interestingly, we find that BERT tends to agree more with annotators than with the corpus when inter-annotator agreement is high but switches to agreeing more with the corpus as inter-annotator agreement drops. We contend that this alignment with annotators, despite being trained on the corpus, suggests that BERT is not memorising article use, but captures a high level generalisation of article use akin to human intuition.
Original languageEnglish
Title of host publicationProceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
Place of PublicationSeattle, United States
PublisherAssociation for Computational Linguistics
Pages924-931
Number of pages8
DOIs
Publication statusPublished - 1 Jul 2022

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