论文标题
使用验证的变压器持续积极学习
Continuous Active Learning Using Pretrained Transformers
论文作者
论文摘要
BERT和T5（T5）（例如Bert和T5）的预训练和微调的变压器模型在临时检索和提问中改善了最新技术的状态，但尚未在高回报信息检索中进行，目的是检索基本所有相关文档。我们调查了将基于变压器的模型用于重读和/或特征化是否可以改善TREC总召回轨道的基线模型实现，这代表了高回复信息检索的当前技术状态。我们还介绍了卡尔伯特（Calbert），该模型可用于基于相关性反馈来连续微调基于BERT的模型。
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.
