WikiOmnia: generative QA corpus on the whole Russian Wikipedia
This provides a scalable solution for QA data generation, addressing annotation bottlenecks for researchers and practitioners in natural language processing, though it is incremental as it builds on existing SQuAD methodology.
The authors tackled the problem of limited training data in General QA by creating WikiOmnia, a fully automated generative pipeline that produced a large-scale QA dataset from the entire Russian Wikipedia, resulting in over 7.9 million raw QA pairs and over 3.4 million cleaned pairs for specific models.
The General QA field has been developing the methodology referencing the Stanford Question answering dataset (SQuAD) as the significant benchmark. However, compiling factual questions is accompanied by time- and labour-consuming annotation, limiting the training data's potential size. We present the WikiOmnia dataset, a new publicly available set of QA-pairs and corresponding Russian Wikipedia article summary sections, composed with a fully automated generative pipeline. The dataset includes every available article from Wikipedia for the Russian language. The WikiOmnia pipeline is available open-source and is also tested for creating SQuAD-formatted QA on other domains, like news texts, fiction, and social media. The resulting dataset includes two parts: raw data on the whole Russian Wikipedia (7,930,873 QA pairs with paragraphs for ruGPT-3 XL and 7,991,040 QA pairs with paragraphs for ruT5-large) and cleaned data with strict automatic verification (over 160,000 QA pairs with paragraphs for ruGPT-3 XL and over 3,400,000 QA pairs with paragraphs for ruT5-large).