面向问答领域的数据增强方法

针对当前自动问答数据增强方法需要大量外部数据的问题,提出一个面向问答模型缺陷的数据增强方法.首先,在训练集上训练好问答模型、问题生成模型以及问答匹配模型;然后,获取问答模型在训练集上预测的所有答案,并选取其中预测错误的答案;再后,使用问题生成模型对这些答案生成相应问题;最后,通过问答匹配模型对生成的问答对进行过滤,保留...

Data Augmentation Method for Question Answering

Aiming at the problem that the current data augmentation method for automatic question answering requires a large amount of external data, a new method oriented to the defects of the question answering model is proposed. Firstly, the question answering (QA) model, question generating (QG) model and question answering matching (QAMatch) model are trained on the training set. Secondly, all the answers predicted by the QA model on the training set are obtained and the wrong ones are selected. Then, the QG model is used to generate corresponding questions for these answers. Finally, the question-answer pairs are filtered by the QAMatch model and the high-quality data are retained as the final augmented data. This method does not require additional data and domain knowledge, and can construct specific data for QA model, improving the performance with less training cost. Experimental results show that the proposed data augmentation method is effective for R-NET, Bert-Base and Luke. Compared with other methods, the QA model achieves better performance improvement with less data scale.