Conversational machine comprehension (CMC) requires understanding the context of multi-turn dialogue. Using BERT, a pre-training language model, has been successful for single-turn machine comprehension, while modeling multiple turns of question answering with BERT has not been established because BERT has a limit on the number and the length of input sequences. In this paper, we propose a simple but effective method with BERT for CMC. Our method uses BERT to encode a paragraph independently conditioned with each question and each answer in a multi-turn context. Then, the method predicts an answer on the basis of the paragraph representations encoded with BERT. The experiments with representative CMC datasets, QuAC and CoQA, show that our method outperformed recently published methods (+0.8 F1 on QuAC and +2.1 F1 on CoQA). In addition, we conducted a detailed analysis of the effects of the number and types of dialogue history on the accuracy of CMC, and we found that the gold answer history, which may not be given in an actual conversation, contributed to the model performance most on both datasets.

本论文提出了一种基于BERT的简单而有效的多轮对话机器理解方法，通过对多个问题和答案的条件编码，预测一个段落表示中的答案。实验结果表明，本方法在QuAC和CoQA数据集上表现优于最近发布的方法。在对对话历史数量和类型的影响进行详细分析后，论文发现金牌答案历史对模型性能的贡献最大。

将多轮上下文与BERT相结合的简单而有效的对话式机器阅读理解方法