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SEINE: SEgment-based Indexing for NEural information retrieval
However, these methods focus on finding the representation that best represents a text (aka metric learning) and the actual retrieval function that is responsible for similarity matching between query and document is kept at a minimum by using dot product.
GazBy: Gaze-Based BERT Model to Incorporate Human Attention in Neural Information Retrieval
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.
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