Technical report on Conversational Question Answering

09/24/2019

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by   Ying Ju, et al.

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Microsoft

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Zhuiyi Shenzhen Chaoyi Technology

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Conversational Question Answering is a challenging task since it requires understanding of conversational history. In this project, we propose a new system RoBERTa + AT +KD, which involves rationale tagging multi-task, adversarial training, knowledge distillation and a linguistic post-process strategy. Our single model achieves 90.4(F1) on the CoQA test set without data augmentation, outperforming the current state-of-the-art single model by 2.6 F1.