@inproceedings{hollenstein-etal-2020-towards,
title = "Towards Best Practices for Leveraging Human Language Processing Signals for Natural Language Processing",
author = "Hollenstein, Nora and
Barrett, Maria and
Beinborn, Lisa",
editor = "Chersoni, Emmanuele and
Devereux, Barry and
Huang, Chu-Ren",
booktitle = "Proceedings of the Second Workshop on Linguistic and Neurocognitive Resources",
month = may,
year = "2020",
address = "Marseille, France",
publisher = "European Language Resources Association",
url = "https://aclanthology.org/2020.lincr-1.3/",
pages = "15--27",
language = "eng",
ISBN = "979-10-95546-52-8",
abstract = "NLP models are imperfect and lack intricate capabilities that humans access automatically when processing speech or reading a text. Human language processing data can be leveraged to increase the performance of models and to pursue explanatory research for a better understanding of the differences between human and machine language processing. We review recent studies leveraging different types of cognitive processing signals, namely eye-tracking, M/EEG and fMRI data recorded during language understanding. We discuss the role of cognitive data for machine learning-based NLP methods and identify fundamental challenges for processing pipelines. Finally, we propose practical strategies for using these types of cognitive signals to enhance NLP models."
}
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%0 Conference Proceedings
%T Towards Best Practices for Leveraging Human Language Processing Signals for Natural Language Processing
%A Hollenstein, Nora
%A Barrett, Maria
%A Beinborn, Lisa
%Y Chersoni, Emmanuele
%Y Devereux, Barry
%Y Huang, Chu-Ren
%S Proceedings of the Second Workshop on Linguistic and Neurocognitive Resources
%D 2020
%8 May
%I European Language Resources Association
%C Marseille, France
%@ 979-10-95546-52-8
%G eng
%F hollenstein-etal-2020-towards
%X NLP models are imperfect and lack intricate capabilities that humans access automatically when processing speech or reading a text. Human language processing data can be leveraged to increase the performance of models and to pursue explanatory research for a better understanding of the differences between human and machine language processing. We review recent studies leveraging different types of cognitive processing signals, namely eye-tracking, M/EEG and fMRI data recorded during language understanding. We discuss the role of cognitive data for machine learning-based NLP methods and identify fundamental challenges for processing pipelines. Finally, we propose practical strategies for using these types of cognitive signals to enhance NLP models.
%U https://aclanthology.org/2020.lincr-1.3/
%P 15-27
Markdown (Informal)
[Towards Best Practices for Leveraging Human Language Processing Signals for Natural Language Processing](https://aclanthology.org/2020.lincr-1.3/) (Hollenstein et al., LiNCr 2020)
ACL