DuReadervis: A : A Chinese Dataset for Open-domain Document Visual Question Answering
Le Qi, Shangwen Lv, Hongyu Li, Jing Liu, Yu Zhang, Qiaoqiao She, Hua Wu, Haifeng Wang, Ting Liu
Abstract
Open-domain question answering has been used in a wide range of applications, such as web search and enterprise search, which usually takes clean texts extracted from various formats of documents (e.g., web pages, PDFs, or Word documents) as the information source. However, designing different text extraction approaches is time-consuming and not scalable. In order to reduce human cost and improve the scalability of QA systems, we propose and study an Open-domain Document Visual Question Answering (Open-domain DocVQA) task, which requires answering questions based on a collection of document images directly instead of only document texts, utilizing layouts and visual features additionally. To advance this task, we introduce the first Chinese Open-domain DocVQA dataset called DuReader vis , containing about 15K question-answering pairs and 158K document images from the Baidu search engine. There are three main challenges in DuReader vis :