AugTriever: Unsupervised Dense Retrieval and Domain Adaptation by Scalable Data Augmentation
This addresses the need for annotation-free training in dense retrieval, offering a scalable solution for researchers and practitioners, though it is incremental as it builds on existing augmentation techniques.
The paper tackled the problem of dense retrieval models requiring extensive human-annotated supervision by proposing unsupervised methods using scalable data augmentation, achieving comparable or better performance than strong baselines on BEIR and ODQA datasets.
Dense retrievers have made significant strides in text retrieval and open-domain question answering. However, most of these achievements have relied heavily on extensive human-annotated supervision. In this study, we aim to develop unsupervised methods for improving dense retrieval models. We propose two approaches that enable annotation-free and scalable training by creating pseudo querydocument pairs: query extraction and transferred query generation. The query extraction method involves selecting salient spans from the original document to generate pseudo queries. On the other hand, the transferred query generation method utilizes generation models trained for other NLP tasks, such as summarization, to produce pseudo queries. Through extensive experimentation, we demonstrate that models trained using these augmentation methods can achieve comparable, if not better, performance than multiple strong dense baselines. Moreover, combining these strategies leads to further improvements, resulting in superior performance of unsupervised dense retrieval, unsupervised domain adaptation and supervised finetuning, benchmarked on both BEIR and ODQA datasets. Code and datasets are publicly available at https://github.com/salesforce/AugTriever.