M3: A Multi-Task Mixed-Objective Learning Framework for Open-Domain
  Multi-Hop Dense Sentence Retrieval
Yang Bai Anthony Colas Christan Grant Daisy Zhe Wang
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:
https://github.com/TonyBY/M3