Connecting degree and polarity: An artificial language learning study
Submission Type: Regular Long Paper
Submission Track: Semantics: Lexical, Sentence level, Document Level, Textual Inference, etc.
Submission Track 2: Linguistic Theories, Cognitive Modeling, and Psycholinguistics
Keywords: semantics, degree, polarity, artificial language learning
Abstract: We investigate a new linguistic generalisation 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 de- gree semantics to polarity sensitivity, including the main one: low degree semantics is associated with preference towards positive polarity.
Submission Number: 1655
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