Rethink Training of BERT Rerankers in Multi-Stage Retrieval Pipeline [article]

Luyu Gao, Zhuyun Dai, Jamie Callan
2021 arXiv   pre-print
Pre-trained deep language models~(LM) have advanced the state-of-the-art of text retrieval. Rerankers fine-tuned from deep LM estimates candidate relevance based on rich contextualized matching signals. Meanwhile, deep LMs can also be leveraged to improve search index, building retrievers with better recall. One would expect a straightforward combination of both in a pipeline to have additive performance gain. In this paper, we discover otherwise and that popular reranker cannot fully exploit
more » ... e improved retrieval result. We, therefore, propose a Localized Contrastive Estimation (LCE) for training rerankers and demonstrate it significantly improves deep two-stage models.
arXiv:2101.08751v1 fatcat:vq6xunkgz5gcjlcgffktbvfquu