论文标题
域的端到端综合数据生成，用于域的适应问题答案系统
End-to-End Synthetic Data Generation for Domain Adaptation of Question Answering Systems
论文作者
论文摘要
我们为合成QA数据生成提出了一种端到端方法。我们的模型包括一个基于变压器的单一编码器 - 码头网络，该网络是端对端训练以生成答案和问题的。简而言之，我们向编码器喂了一段段落，并要求解码器产生一个问题和一个逐个答案。生成过程中产生的可能性被用作滤波得分，这避免了对单独的过滤模型的需求。我们的发电机通过使用最大似然估计来微调LM进行训练。实验结果表明，质量检查模型的域适应性的显着改善优于当前最新方法。
We propose an end-to-end approach for synthetic QA data generation. Our model comprises a single transformer-based encoder-decoder network that is trained end-to-end to generate both answers and questions. In a nutshell, we feed a passage to the encoder and ask the decoder to generate a question and an answer token-by-token. The likelihood produced in the generation process is used as a filtering score, which avoids the need for a separate filtering model. Our generator is trained by fine-tuning a pretrained LM using maximum likelihood estimation. The experimental results indicate significant improvements in the domain adaptation of QA models outperforming current state-of-the-art methods.
