Barend Beekhuizen


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What social attitudes about gender does BERT encode? Leveraging insights from psycholinguistics
Julia Watson | Barend Beekhuizen | Suzanne Stevenson
Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Much research has sought to evaluate the degree to which large language models reflect social biases. We complement such work with an approach to elucidating the connections between language model predictions and people’s social attitudes. We show how word preferences in a large language model reflect social attitudes about gender, using two datasets from human experiments that found differences in gendered or gender neutral word choices by participants with differing views on gender (progressive, moderate, or conservative). We find that the language model BERT takes into account factors that shape human lexical choice of such language, but may not weigh those factors in the same way people do. Moreover, we show that BERT’s predictions most resemble responses from participants with moderate to conservative views on gender. Such findings illuminate how a language model: (1) may differ from people in how it deploys words that signal gender, and (2) may prioritize some social attitudes over others.


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Remodelling complement coercion interpretation
Frederick Gietz | Barend Beekhuizen
Proceedings of the Society for Computation in Linguistics 2022


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A Formidable Ability: Detecting Adjectival Extremeness with DSMs
Farhan Samir | Barend Beekhuizen | Suzanne Stevenson
Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021


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Say Anything: Automatic Semantic Infelicity Detection in L2 English Indefinite Pronouns
Ella Rabinovich | Julia Watson | Barend Beekhuizen | Suzanne Stevenson
Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL)

Computational research on error detection in second language speakers has mainly addressed clear grammatical anomalies typical to learners at the beginner-to-intermediate level. We focus instead on acquisition of subtle semantic nuances of English indefinite pronouns by non-native speakers at varying levels of proficiency. We first lay out theoretical, linguistically motivated hypotheses, and supporting empirical evidence, on the nature of the challenges posed by indefinite pronouns to English learners. We then suggest and evaluate an automatic approach for detection of atypical usage patterns, demonstrating that deep learning architectures are promising for this task involving nuanced semantic anomalies.


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Perceptual, conceptual, and frequency effects on error patterns in English color term acquisition
Barend Beekhuizen | Suzanne Stevenson
Proceedings of the Sixth Workshop on Cognitive Aspects of Computational Language Learning


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A Usage-Based Model of Early Grammatical Development
Barend Beekhuizen | Rens Bod | Afsaneh Fazly | Suzanne Stevenson | Arie Verhagen
Proceedings of the Fifth Workshop on Cognitive Modeling and Computational Linguistics