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- W4284714439 abstract "This paper is interested in investigating whether human gaze signals can be leveraged to improve state-of-the-art search engine performance and how to incorporate this new input signal marked by human attention into existing neural retrieval models. In this paper, we propose GazBy (Gaze-based Bert model for document relevancy), a lightweight joint model that integrates human gaze fixation estimation into transformer models to predict document relevance, incorporating more nuanced information about cognitive processing into information retrieval (IR). We evaluate our model on the Text Retrieval Conference (TREC) Deep Learning (DL) 2019 and 2020 Tracks. Our experiments show encouraging results and illustrate the effective and ineffective entry points for using human gaze to help with transformer-based neural 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 a first step exploring using gaze signals in modern neural search engines." @default.
- W4284714439 created "2022-07-08" @default.
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- W4284714439 date "2022-08-23" @default.
- W4284714439 modified "2023-09-28" @default.
- W4284714439 title "GazBy: Gaze-Based BERT Model to Incorporate Human Attention in Neural Information Retrieval" @default.
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- W4284714439 doi "https://doi.org/10.1145/3539813.3545129" @default.
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