@inproceedings{jaffe-etal-2021-coreference-aware,
title = "Coreference-aware Surprisal Predicts Brain Response",
author = "Jaffe, Evan and
Oh, Byung-Doh and
Schuler, William",
editor = "Moens, Marie-Francine and
Huang, Xuanjing and
Specia, Lucia and
Yih, Scott Wen-tau",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2021",
month = nov,
year = "2021",
address = "Punta Cana, Dominican Republic",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.findings-emnlp.285/",
doi = "10.18653/v1/2021.findings-emnlp.285",
pages = "3351--3356",
abstract = "Recent evidence supports a role for coreference processing in guiding human expectations about upcoming words during reading, based on covariation between reading times and word surprisal estimated by a coreference-aware semantic processing model (Jaffe et al. 2020).The present study reproduces and elaborates on this finding by (1) enabling the parser to process subword information that might better approximate human morphological knowledge, and (2) extending evaluation of coreference effects from self-paced reading to human brain imaging data. Results show that an expectation-based processing effect of coreference is still evident even in the presence of the stronger psycholinguistic baseline provided by the subword model, and that the coreference effect is observed in both self-paced reading and fMRI data, providing evidence of the effect`s robustness."
}
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<abstract>Recent evidence supports a role for coreference processing in guiding human expectations about upcoming words during reading, based on covariation between reading times and word surprisal estimated by a coreference-aware semantic processing model (Jaffe et al. 2020).The present study reproduces and elaborates on this finding by (1) enabling the parser to process subword information that might better approximate human morphological knowledge, and (2) extending evaluation of coreference effects from self-paced reading to human brain imaging data. Results show that an expectation-based processing effect of coreference is still evident even in the presence of the stronger psycholinguistic baseline provided by the subword model, and that the coreference effect is observed in both self-paced reading and fMRI data, providing evidence of the effect‘s robustness.</abstract>
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%0 Conference Proceedings
%T Coreference-aware Surprisal Predicts Brain Response
%A Jaffe, Evan
%A Oh, Byung-Doh
%A Schuler, William
%Y Moens, Marie-Francine
%Y Huang, Xuanjing
%Y Specia, Lucia
%Y Yih, Scott Wen-tau
%S Findings of the Association for Computational Linguistics: EMNLP 2021
%D 2021
%8 November
%I Association for Computational Linguistics
%C Punta Cana, Dominican Republic
%F jaffe-etal-2021-coreference-aware
%X Recent evidence supports a role for coreference processing in guiding human expectations about upcoming words during reading, based on covariation between reading times and word surprisal estimated by a coreference-aware semantic processing model (Jaffe et al. 2020).The present study reproduces and elaborates on this finding by (1) enabling the parser to process subword information that might better approximate human morphological knowledge, and (2) extending evaluation of coreference effects from self-paced reading to human brain imaging data. Results show that an expectation-based processing effect of coreference is still evident even in the presence of the stronger psycholinguistic baseline provided by the subword model, and that the coreference effect is observed in both self-paced reading and fMRI data, providing evidence of the effect‘s robustness.
%R 10.18653/v1/2021.findings-emnlp.285
%U https://aclanthology.org/2021.findings-emnlp.285/
%U https://doi.org/10.18653/v1/2021.findings-emnlp.285
%P 3351-3356
Markdown (Informal)
[Coreference-aware Surprisal Predicts Brain Response](https://aclanthology.org/2021.findings-emnlp.285/) (Jaffe et al., Findings 2021)
ACL
- Evan Jaffe, Byung-Doh Oh, and William Schuler. 2021. Coreference-aware Surprisal Predicts Brain Response. In Findings of the Association for Computational Linguistics: EMNLP 2021, pages 3351–3356, Punta Cana, Dominican Republic. Association for Computational Linguistics.