Low-cost eye tracking corpus for explainable natural language processing
This infrastructure project enables the creation of a multilingual low-cost eye-tracking dataset, designed to make artificial intelligence-based language technologies fair and transparent. Deep learning models are ubiquitous in the present-day landscape of artificial intelligence. In order to understand what these models learn despite their generally opaque nature, and whether their rationales align with those of humans, we collect low-cost eye movement data through a crowd-sourcing platform and learn human rationales from gaze patterns. We collect gaze patterns from both task-specific and task-agnostic natural reading, providing a platform for evaluating the transparency and soundness of modern language technologies at scale.
PIs: Anders Søgaard, DIKU and Nora Hollenstein, CST, NorS.
Assistant: Tiago Ribeiro
Project period: February 2022 to May 2023.
Funded by Carlsberg Foundation.