AffiliationUniversity of Arizona, Department of Linguistics
University of Arizona, School of Information
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CitationZhao, Y., Ngui, J. G., Hartley, L. H., & Bethard, S. (2021, November). Do pretrained transformers infer telicity like humans?. In Proceedings of the 25th Conference on Computational Natural Language Learning (pp. 72-81).
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AbstractPretrained transformer-based language models achieve state-of-the-art performance in many NLP tasks, but it is an open question whether the knowledge acquired by the models during pretraining resembles the linguistic knowledge of humans. We present both humans and pretrained transformers with descriptions of events, and measure their preference for telic interpretations (the event has a natural endpoint) or atelic interpretations (the event does not have a natural endpoint). To measure these preferences and determine what factors influence them, we design an English test and a novel-word test that include a variety of linguistic cues (noun phrase quantity, resultative structure, contextual information, temporal units) that bias toward certain interpretations. We find that humans’ choice of telicity interpretation is reliably influenced by theoretically-motivated cues, transformer models (BERT and RoBERTa) are influenced by some (though not all) of the cues, and transformer models often rely more heavily on temporal units than humans do. © 2021 Association for Computational Linguistics.
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Except where otherwise noted, this item's license is described as © 2021 Association for Computational Linguistics, licensed on a Creative Commons Attribution 4.0 International License.