论文标题
V-DOC：带有文档的视觉问题答案
V-Doc : Visual questions answers with Documents
论文作者
论文摘要
我们建议使用文档图像和PDF进行V-DOC，这是一种提问工具，主要针对研究人员和一般的非深度学习专家，希望生成，处理和理解文档的视觉问题回答任务。 V-DOC支持使用文档图像生成和使用提取性和抽象的提问答案对。挖掘质量检查从文档内容中选择一个令牌或短语的子集来预测答案，而抽象质量请允许识别内容中的语言并基于训练有素的模型生成答案。这两个方面对于理解文档，尤其是图像格式都至关重要。我们为抽象质量检查任务提供了问题生成的详细方案。 V-DOC支持广泛的数据集和模型，并且通过声明性的，框架 - 不合Snostic平台可扩展。
We propose V-Doc, a question-answering tool using document images and PDF, mainly for researchers and general non-deep learning experts looking to generate, process, and understand the document visual question answering tasks. The V-Doc supports generating and using both extractive and abstractive question-answer pairs using documents images. The extractive QA selects a subset of tokens or phrases from the document contents to predict the answers, while the abstractive QA recognises the language in the content and generates the answer based on the trained model. Both aspects are crucial to understanding the documents, especially in an image format. We include a detailed scenario of question generation for the abstractive QA task. V-Doc supports a wide range of datasets and models, and is highly extensible through a declarative, framework-agnostic platform.
