Work

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

Year: 2020

Type: conference-paper

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.

Source: Research at the University of Copenhagen (University of Copenhagen)

Authors: Nora Hollenstein, Maria Barrett, Lisa Beinborn

Institution: University of Copenhagen

Language: English

Topic: Natural Language Processing Techniques

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

Funder: Nederlandse Organisatie voor Wetenschappelijk Onderzoek

Award: G2084304408

OpenAlex ID: W3087970256