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Evaluating Webcam-based Gaze Data as an Alternative for Human Rationale Annotations
March 1, 2024, 5:49 a.m. | Stephanie Brandl, Oliver Eberle, Tiago Ribeiro, Anders S{\o}gaard, Nora Hollenstein
cs.CL updates on arXiv.org arxiv.org
Abstract: Rationales in the form of manually annotated input spans usually serve as ground truth when evaluating explainability methods in NLP. They are, however, time-consuming and often biased by the annotation process. In this paper, we debate whether human gaze, in the form of webcam-based eye-tracking recordings, poses a valid alternative when evaluating importance scores. We evaluate the additional information provided by gaze data, such as total reading times, gaze entropy, and decoding accuracy with respect …
abstract annotation annotations arxiv cs.cl data explainability form human nlp paper process serve truth type webcam
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