论文标题
MTSS：向多个领域教师学习，并成为多域对话专家
MTSS: Learn from Multiple Domain Teachers and Become a Multi-domain Dialogue Expert
论文作者
论文摘要
如何建立高质量的多域对话系统是一项艰巨的工作，这是由于其复杂而纠缠的对话状态空间在每个领域之间，这严重限制了对话政策的质量，并进一步影响了生成的响应。在本文中，我们提出了一种新颖的方法，以获取令人满意的政策，并在多域名环境中巧妙地避免了对话状态表示问题。受到真正的学校教学场景的启发，我们的方法由多个特定领域的教师和一个普遍的学生组成。每个教师只专注于一个特定领域，并根据精确提取的单个域对话状态表示，了解其相应的领域知识和对话政策。然后，这些特定领域的教师将其领域知识和政策授予通用学生模型，并集体使该学生模型成为多域对话专家。实验结果表明，我们的方法在多域和单个域设置中都与SOTA达到竞争结果。
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.
