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This paper is interested in investigating whether human gaze signals can beleveraged to improve state-of-the-art search engine performance and how toincorporate this new input signal marked by human attention into existingneural retrieval models. In this paper, we propose GazBy ({\bf Gaz}e-based {\bfB}ert model for document relevanc{\bf y}), a light-weight joint model thatintegrates human gaze fixation estimation into transformer models to predictdocument relevance, incorporating more nuanced information about cognitiveprocessing into information retrieval (IR). We evaluate our model on the TextRetrieval Conference (TREC) Deep Learning (DL) 2019 and 2020 Tracks. Ourexperiments show encouraging results and illustrate the effective andineffective entry points for using human gaze to help with transformer-basedneural retrievers. With the rise of virtual reality (VR) and augmented reality(AR), human gaze data will become more available. We hope this work serves as afirst step exploring using gaze signals in modern neural search engines.

GazBy: Gaze-Based BERT Model to Incorporate Human Attention in Neural Information Retrieval