Towards Best Practices for Leveraging Human Language Processing Signals for Natural Language Processing

Nora Hollenstein, Maria Barrett, Lisa Beinborn


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.
Anthology ID:
2020.lincr-1.3
Volume:
Proceedings of the Second Workshop on Linguistic and Neurocognitive Resources
Month:
May
Year:
2020
Address:
Marseille, France
Venues:
LREC | LiNCr | WS
SIG:
Publisher:
European Language Resources Association
Note:
Pages:
15–27
Language:
English
URL:
https://www.aclweb.org/anthology/2020.lincr-1.3
DOI:
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PDF:
http://aclanthology.lst.uni-saarland.de/2020.lincr-1.3.pdf