论文标题
通过辅助任务学习一个简单有效的模型，以生成多转响应的生成
Learning a Simple and Effective Model for Multi-turn Response Generation with Auxiliary Tasks
论文作者
论文摘要
我们研究开放域对话的多转反应生成。现有的最先进的方法解决了深层神经体系结构的问题。尽管这些模型提高了响应质量，但它们的复杂性也阻碍了模型在实际系统中的应用。在这项工作中，我们追求一个具有简单结构但可以有效利用响应生成的对话环境的模型。为此，我们提出了四个辅助任务，包括单词顺序恢复，话语顺序恢复，掩盖的单词恢复和掩盖的话语恢复，并优化这些任务的目标，并最大程度地提高生成的可能性。通过这种方式，与上下文理解相关的辅助任务可以指导生成模型的学习以实现更好的本地最佳效果。具有三个基准的经验研究表明，我们的模型可以在自动评估和人类判断的响应质量方面显着超过最先进的生成模型，同时又享有更快的解码过程。
We study multi-turn response generation for open-domain dialogues. The existing state-of-the-art addresses the problem with deep neural architectures. While these models improved response quality, their complexity also hinders the application of the models in real systems. In this work, we pursue a model that has a simple structure yet can effectively leverage conversation contexts for response generation. To this end, we propose four auxiliary tasks including word order recovery, utterance order recovery, masked word recovery, and masked utterance recovery, and optimize the objectives of these tasks together with maximizing the likelihood of generation. By this means, the auxiliary tasks that relate to context understanding can guide the learning of the generation model to achieve a better local optimum. Empirical studies with three benchmarks indicate that our model can significantly outperform state-of-the-art generation models in terms of response quality on both automatic evaluation and human judgment, and at the same time enjoys a much faster decoding process.
