Decoding Brain Activity Associated with Literal and Metaphoric Sentence Comprehension Using Distributional Semantic Models

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G. Djokic, Vesna; Maillard, Jean; Bulat, Luana and Shutova, Ekaterina. 2020. Decoding Brain Activity Associated with Literal and Metaphoric Sentence Comprehension Using Distributional Semantic Models. Transactions of the Association for Computational Linguistics, 8, pp. 231-246. ISSN 2307-387X [Article]

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Official URL: https://www.mitpressjournals.org/doi/full/10.1162/...

Abstract or Description

Recent years have seen a growing interest within the natural language processing (NLP)community in evaluating the ability of semantic models to capture human meaning representation in the brain. Existing research has mainly focused on applying semantic models to de-code brain activity patterns associated with the meaning of individual words, and, more recently, this approach has been extended to sentences and larger text fragments. Our work is the first to investigate metaphor process-ing in the brain in this context. We evaluate a range of semantic models (word embeddings, compositional, and visual models) in their ability to decode brain activity associated with reading of both literal and metaphoric sentences. Our results suggest that compositional models and word embeddings are able to capture differences in the processing of literal and metaphoric sentences, providing sup-port for the idea that the literal meaning is not fully accessible during familiar metaphor comprehension.

Item Type:

Article

Identification Number (DOI):

https://doi.org/10.1162/tacl_a_00307