论文标题
如有疑问，请问：产生答案和无法回答的问题，无监督
When in Doubt, Ask: Generating Answerable and Unanswerable Questions, Unsupervised
论文作者
论文摘要
问题回答（QA）是使人与机器之间进行牢固沟通的关键。用于质量检查的现代语言模型在几项基本任务中超过了人为的绩效。但是，这些模型需要大量的人类生成的培训数据，这些数据既昂贵又耗时。本文研究了使用合成数据来增强人造数据集，以此来解决此问题。基于深层变压器的最先进模型用于检查使用合成的答案和无法回答的问题来补充著名的人为数据集的影响。结果表明，在混合数据集上训练的语言模型的性能（以F1和EM分数衡量）进行了切实的改善。具体而言，无法回答的问题提示在提高模型方面更有效：F1分数从添加到原始数据集中的可回答，无法回答和合并的问题提出的提问分别为1.3％，5.0％和6.7％。 [链接到GitHub存储库：https：//github.com/lnikolenko/eqa]
Question Answering (QA) is key for making possible a robust communication between human and machine. Modern language models used for QA have surpassed the human-performance in several essential tasks; however, these models require large amounts of human-generated training data which are costly and time-consuming to create. This paper studies augmenting human-made datasets with synthetic data as a way of surmounting this problem. A state-of-the-art model based on deep transformers is used to inspect the impact of using synthetic answerable and unanswerable questions to complement a well-known human-made dataset. The results indicate a tangible improvement in the performance of the language model (measured in terms of F1 and EM scores) trained on the mixed dataset. Specifically, unanswerable question-answers prove more effective in boosting the model: the F1 score gain from adding to the original dataset the answerable, unanswerable, and combined question-answers were 1.3%, 5.0%, and 6.7%, respectively. [Link to the Github repository: https://github.com/lnikolenko/EQA]
