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arXiv · 2208.06955

Continuous Active Learning Using Pretrained Transformers

Nima Sadri·Gordon V. Cormack
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Abstract

Pre-trained and fine-tuned transformer models like BERT and T5 have improved the state of the art in ad-hoc retrieval and question-answering, but not as yet in high-recall information retrieval, where the objective is to retrieve substantially all relevant documents. We investigate whether the use of transformer-based models for reranking and/or featurization can improve the Baseline Model Implementation of the TREC Total Recall Track, which represents the current state of the art for high-recall information retrieval. We also introduce CALBERT, a model that can be used to continuously fine-tune a BERT-based model based on relevance feedback.

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BibTeXRIS

Nima Sadri, Gordon V. Cormack. 2022-08-15. Continuous Active Learning Using Pretrained Transformers. https://arxiv.org/abs/2208.06955

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