@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://preview.aclanthology.org/fix-sig-urls/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."
}