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.
| Original language | English |
|---|---|
| Title of host publication | Proceedings of the Second Workshop on Linguistic and Neurocognitive Resources |
| Place of Publication | Marseille, France |
| Publisher | European Language Resources Association |
| Pages | 15-27 |
| Number of pages | 13 |
| ISBN (Print) | 9791095546528 |
| Publication status | Published - May 2020 |
