Embedding-based Zero-shot Retrieval through Query Generation
Abstract
Passage retrieval addresses the problem of locating relevant passages, usually from a large corpus, given a query. In practice, lexical term-matching algorithms like BM25 are popular choices for retrieval owing to their efficiency. However, term-based matching algorithms often miss relevant passages that have no lexical overlap with the query and cannot be finetuned to downstream datasets. In this work, we consider the embedding-based two-tower architecture as our neural retrieval model. Since labeled data can be scarce and because neural retrieval models require vast amounts of data to train, we propose a novel method for generating synthetic training data for retrieval. Our system produces remarkable results, significantly outperforming BM25 on 5 out of 6 datasets tested, by an average of 2.45 points for Recall@1. In some cases, our model trained on synthetic data can even outperform the same model trained on real data
Cite
@article{arxiv.2009.10270,
title = {Embedding-based Zero-shot Retrieval through Query Generation},
author = {Davis Liang and Peng Xu and Siamak Shakeri and Cicero Nogueira dos Santos and Ramesh Nallapati and Zhiheng Huang and Bing Xiang},
journal= {arXiv preprint arXiv:2009.10270},
year = {2020}
}