Connecting degree and polarity: An artificial language learning study
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Abstract (arXiv)
We investigate a new linguistic generalization in pre-trained language models (taking BERT (Devlin et al., 2019) as a case study). We focus on degree modifiers (expressions like slightly, very, rather, extremely) and test the hypothesis that the degree expressed by a modifier (low, medium or high degree) is related to the modifier's sensitivity to sentence polarity (whether it shows preference for affirmative or negative sentences or neither). To probe this connection, we apply the Artificial Language Learning experimental paradigm from psycholinguistics to a neural language model. Our experimental results suggest that BERT generalizes in line with existing linguistic observations that relate degree semantics to polarity sensitivity, including the main one: low degree semantics is associated with preference towards positive polarity.
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References · 12
- 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
- 2014Adam: A Method for Stochastic Optimizationcited by 37,240
- 2020Language Models are Few-Shot Learnerscited by 32,419
- 2018Deep contextualized word representationscited by 2,290
- 2019Learning the Difference that Makes a Difference with Counterfactually-Augmented Datacited by 439
- 2019Neural Language Models as Psycholinguistic Subjects: Representations of Syntactic Statecited by 138
- 2020Explaining The Efficacy of Counterfactually Augmented Datacited by 65
- 2019Investigating BERT's Knowledge of Language: Five Analysis Methods with NPIscited by 58
- 2018Do Language Models Understand Anything? On the Ability of LSTMs to Understand Negative Polarity Itemscited by 36
- 2021Language Models Use Monotonicity to Assess NPI Licensingcited by 31
- 2020Investigating Novel Verb Learning in BERT: Selectional Preference Classes and Alternation-Based Syntactic Generalizationcited by 16
- 2020Structural Supervision Improves Few-Shot Learning and Syntactic Generalization in Neural Language Modelscited by 8
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