In multi-turn dialog, utterances do not always take the full form of sentences \cite{Carbonell1983DiscoursePA}, which naturally makes understanding the dialog context more difficult. However, it is essential to fully grasp the dialog context to generate a reasonable response. Hence, in this paper, we propose to improve the response generation performance by examining the model's ability to answer a reading comprehension question, where the question is focused on the omitted information in the dialog. Enlightened by the multi-task learning scheme, we propose a joint framework that unifies these two tasks, sharing the same encoder to extract the common and task-invariant features with different decoders to learn task-specific features. To better fusing information from the question and the dialog history in the encoding part, we propose to augment the Transformer architecture with a memory updater, which is designed to selectively store and update the history dialog information so as to support downstream tasks. For the experiment, we employ human annotators to write and examine a large-scale dialog reading comprehension dataset. Extensive experiments are conducted on this dataset, and the results show that the proposed model brings substantial improvements over several strong baselines on both tasks. In this way, we demonstrate that reasoning can indeed help better response generation and vice versa. We release our large-scale dataset for further research.

本文提出一种联合框架，用于改善多轮对话中回复生成的表现。这种框架结合了阅读理解任务和回复生成任务，共享相同的编码器来提取共同且与任务无关的特征和不同的解码器来学习任务特定的特征。用记忆更新器增强Transformer体系结构以更好地将问题和对话历史的信息融合在编码部分，并在大规模对话阅读理解数据集上进行了实验，结果显示该模型对于两个任务都带来了显著的改进。

对话推理：通过上下文阅读理解提升响应生成