Unsupervised Dense Retrieval Deserves Better Positive Pairs: Scalable
Augmentation with Query Extraction and Generation
- URL: http://arxiv.org/abs/2212.08841v1
- Date: Sat, 17 Dec 2022 10:43:25 GMT
- Title: Unsupervised Dense Retrieval Deserves Better Positive Pairs: Scalable
Augmentation with Query Extraction and Generation
- Authors: Rui Meng, Ye Liu, Semih Yavuz, Divyansh Agarwal, Lifu Tu, Ning Yu,
Jianguo Zhang, Meghana Bhat, Yingbo Zhou
- Abstract summary: We explore two categories of methods for creating pseudo query-document pairs, named query extraction (QExt) and transferred query generation (TQGen)
QExt extracts pseudo queries by document structures or selecting salient random spans, and TQGen utilizes generation models trained for other NLP tasks.
Experiments show that dense retrievers trained with individual augmentation methods can perform comparably well with multiple strong baselines.
- Score: 27.391814046104646
- License: http://creativecommons.org/licenses/by-nc-sa/4.0/
- Abstract: Dense retrievers have made significant strides in obtaining state-of-the-art
results on text retrieval and open-domain question answering (ODQA). Yet most
of these achievements were made possible with the help of large annotated
datasets, unsupervised learning for dense retrieval models remains an open
problem. In this work, we explore two categories of methods for creating pseudo
query-document pairs, named query extraction (QExt) and transferred query
generation (TQGen), to augment the retriever training in an annotation-free and
scalable manner. Specifically, QExt extracts pseudo queries by document
structures or selecting salient random spans, and TQGen utilizes generation
models trained for other NLP tasks (e.g., summarization) to produce pseudo
queries. Extensive experiments show that dense retrievers trained with
individual augmentation methods can perform comparably well with multiple
strong baselines, and combining them leads to further improvements, achieving
state-of-the-art performance of unsupervised dense retrieval on both BEIR and
ODQA datasets.
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