Context modeling has a pivotal role in open domain conversation. Existing works either use heuristic methods or jointly learn context modeling and response generation with an encoder-decoder framework. This paper proposes an explicit context rewriting method, which rewrites the last utterance by considering context history. We leverage pseudo-parallel data and elaborate a context rewriting network, which is built upon the CopyNet with the reinforcement learning method. The rewritten utterance is beneficial to candidate retrieval, explainable context modeling, as well as enabling to employ a single-turn framework to the multi-turn scenario. The empirical results show that our model outperforms baselines in terms of the rewriting quality, the multi-turn response generation, and the end-to-end retrieval-based chatbots.
翻译:上下文建模在开放域域对话中具有关键作用。 现有的作品要么使用超常方法,要么用编码器- 编码器框架共同学习背景建模和反应生成。 本文提出一个明确的上下文重写方法,该方法通过考虑上下文历史重写最后一句话。 我们利用假平行数据,并精心设计一个环境重写网络,以复制网络为基础,采用强化学习方法。 改写语有利于候选人检索、可解释的上下文建模以及能够对多转假设采用单向框架。 实证结果显示,我们的模型在重写质量、多转反应生成和终端到终端检索聊天器方面超过了基线。