Information Retrieval (IR) constitutes a vital facet of Open Domain Question Answering (ODQA) systems, focusing on the exploration of pertinent information within extensive collections of passages, such as Wikipedia, to facilitate subsequent reader processing. Historically, IR relied on textual overlaps for relevant context retrieval, employing methods like BM25 and TF-IDF, which, however, lacked natural language understanding. The advent of deep learning ushered in a new era, leading to the introduction of Dense Passage Retrievers (DPR), shows superiority over traditional sparse retrievers. These dense retrievers leverage Pre-Trained Language Models (PLMs) to initialize context encoders, enabling the extraction of natural language representations. They utilize the distance between latent vectors of contexts as a metric for assessing similarity. However, DPR methods are heavily reliant on large volumes of meticulously labeled data, such as Natural Questions. The process of data labeling is both costly and time-intensive. In this article, we propose a novel data augmentation methodology Self Data Augmentation (SDA) that employs DPR models to automatically annotate unanswered questions. Specifically, we initiate the process by retrieving relevant pseudo passages for these unlabeled questions. We subsequently introduce three distinct passage selection methods to annotate these pseudo passages. Ultimately, we amalgamate the pseudo-labeled passages with the unanswered questions to create augmented data. Our experimental evaluations conducted on two extensive datasets (Natural Questions and TriviaQA), alongside a relatively small dataset (WebQuestions), utilizing three diverse base models, illustrate the significant enhancement achieved through the incorporation of freshly augmented data. Moreover, our proposed data augmentation method exhibits remarkable flexibility, which is readily adaptable to various dense retrievers. Additionally, we have conducted a comprehensive human study on the augmented data, which further supports our conclusions.

Qin Zhang, Shangsi Chen, Dongkuan Xu, Qingqing Cao, Xiaojun Chen, Trevor Cohn, Meng Fang. Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2023.