In recent research, contrastive learning has proven to be a highly effective
method for representation learning and is widely used for dense retrieval.
However, we identify that relying solely on contrastive learning can lead to
suboptimal retrieval performance. On the other hand, despite many retrieval
datasets supporting various learning objectives beyond contrastive learning,
combining them efficiently in multi-task learning scenarios can be challenging.
In this paper, we introduce M3, an advanced recursive Multi-hop dense sentence
retrieval system built upon a novel Multi-task Mixed-objective approach for
dense text representation learning, addressing the aforementioned challenges.
Our approach yields state-of-the-art performance on a large-scale open-domain
fact verification benchmark dataset, FEVER. Code and data are available at:
this https URL

本文介绍了 M3，一种基于多任务混合目标方法的高级递归多跳密集句检索系统，用于密集文本表示学习，能够有效地解决基于对比学习的检索性能问题，并在大规模开放领域事实验证基准数据集 FEVER 上取得了最先进的性能。