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Interactive Assessment Tool for Gaze-based Machine Learning Models in Information Retrieval

Pablo Valdunciel; Omair Shahzad Bhatti; Michael Barz; Daniel Sonntag
In: ACM SIGIR Conference on Human Information Interaction and Retrieval. ACM SIGIR Conference on Human Information Interaction and Retrieval (CHIIR-2022), March 14-18, Regensburg, Germany, ISBN 9781450391863, Association for Computing Machinery, 3/2022.


Eye movements were shown to be an effective source of implicit relevance feedback in information retrieval tasks. They can be used to, e.g., estimate the relevance of read documents and expand search queries using machine learning. In this paper, we present the Reading Model Assessment tool (ReMA), an interactive tool for assessing gaze-based relevance estimation models. Our tool allows experimenters to easily browse recorded trials, compare the model output to a ground truth, and visualize gaze-based features at the token- and paragraph-level that serve as model input. Our goal is to facilitate the understanding of the relation between eye movements and the human relevance estimation process, to understand the strengths and weaknesses of a model at hand, and, eventually, to enable researchers to build more effective models.