id	author	title	date	pages	extension	mime	words	sentences	flesch	summary	cache	txt
cord-020843-cq4lbd0l	Almeida, Tiago	Calling Attention to Passages for Biomedical Question Answering	2020-03-24		.txt	text/plain	2235	125	50	This paper presents a pipeline for document and passage retrieval for biomedical question answering built around a new variant of the DeepRank network model in which the recursive layer is replaced by a self-attention layer combined with a weighting mechanism. On the other hand, models such as the Deep Relevance Matching Model (DRMM) [3] or DeepRank [10] follow a interaction-based approach, in which matching signals between query and document are captured and used by the neural network to produces a ranking score. The main contribution of this work is a new variant of the DeepRank neural network architecture in which the recursive layer originally included in the final aggregation step is replaced by a self-attention layer followed by a weighting mechanism similar to the term gating layer of the DRMM. The proposed model was evaluated on the BioASQ dataset, as part of a document and passage (snippet) retrieval pipeline for biomedical question answering, achieving similar retrieval performance when compared to more complex network architectures.	./cache/cord-020843-cq4lbd0l.txt	./txt/cord-020843-cq4lbd0l.txt