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Technical Report On Conversational Question Answering

Ying Ju, Fubang Zhao, Shijie Chen, Bowen Zheng, Xuefeng Yang, Yunfeng Liu . Arxiv 2019 – 46 citations

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Efficiency Evaluation Question Answering Security Training Techniques

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.

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