MTSS：向多位域老师学习，并成为多域对话专家（CS CL）---刘子蔚

如何建立高质量的多域对话系统是一项具有挑战性的工作，因为每个域之间的对话状态空间复杂且纠缠不清，这严重限制了对话策略的质量，并进一步影响了所产生的响应。在本文中，我们提出了一种新颖的方法来获得令人满意的策略并巧妙地规避多域设置中的疑难对话状态表示问题。受实际学校教学场景的启发，我们的方法由多名特定领域的老师和一名全能学生组成。每个老师只专注于一个特定领域，并基于精确提取的单个领域对话状态表示来学习其相应的领域知识和对话策略。然后，这些特定领域的老师将他们的领域知识和政策传授给通用的学生模型，并共同使该学生模型成为多领域对话专家。实验结果表明，在多域和单域设置中，我们的方法都可以与SOTA取得竞争性结果。

原文标题：MTSS: Learn from Multiple Domain Teachers and Become a Multi-domain Dialogue Expert

原文：How to build a high-quality multi-domain dialogue system is a challenging work due to its complicated and entangled dialogue state space among each domain, which seriously limits the quality of dialogue policy, and further affects the generated response. In this paper, we propose a novel method to acquire a satisfying policy and subtly circumvent the knotty dialogue state representation problem in the multi-domain setting. Inspired by real school teaching scenarios, our method is composed of multiple domain-specific teachers and a universal student. Each individual teacher only focuses on one specific domain and learns its corresponding domain knowledge and dialogue policy based on a precisely extracted single domain dialogue state representation. Then, these domain-specific teachers impart their domain knowledge and policies to a universal student model and collectively make this student model a multi-domain dialogue expert. Experiment results show that our method reaches competitive results with SOTAs in both multi-domain and single domain setting.

原文作者：Shuke Peng, Feng Ji, Zehao Lin, Shaobo Cui, Haiqing Chen, Yin Zhang

原文地址：https://arxiv.org/abs/2005.10450