Contextualization with SPLADE for High Recall Retrieval

• URL: http://arxiv.org/abs/2405.03972v1
• Date: Tue, 07 May 2024 03:05:37 GMT
• Title: Contextualization with SPLADE for High Recall Retrieval
• Authors: Eugene Yang,
• Abstract summary: High Recall Retrieval (HRR) is a search problem that optimize the cost of retrieving most relevant documents in a given collection. In this work, we leverage SPLADE, an efficient retrieval model that transforms documents into contextualized sparse vectors. It reduces 10% and 18% of the review cost in two HRR evaluation collections under a one-phase review workflow with a target recall of 80%.
• Score: 5.973857434357868
• License:
• Abstract: High Recall Retrieval (HRR), such as eDiscovery and medical systematic review, is a search problem that optimizes the cost of retrieving most relevant documents in a given collection. Iterative approaches, such as iterative relevance feedback and uncertainty sampling, are shown to be effective under various operational scenarios. Despite neural models demonstrating success in other text-related tasks, linear models such as logistic regression, in general, are still more effective and efficient in HRR since the model is trained and retrieves documents from the same fixed collection. In this work, we leverage SPLADE, an efficient retrieval model that transforms documents into contextualized sparse vectors, for HRR. Our approach combines the best of both worlds, leveraging both the contextualization from pretrained language models and the efficiency of linear models. It reduces 10% and 18% of the review cost in two HRR evaluation collections under a one-phase review workflow with a target recall of 80%. The experiment is implemented with TARexp and is available at https://github.com/eugene-yang/LSR-for-TAR.

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