We participate in the DSTC9 Interactive Dialogue Evaluation Track (Gunasekara
et al. 2020) sub-task 1 (Knowledge Grounded Dialogue) and sub-task 2
(Interactive Dialogue). In sub-task 1, we employ a pre-trained language model
to generate topic-related responses and propose a response ensemble method for
response selection. In sub-task2, we propose a novel Dialogue Planning Model
(DPM) to capture conversation flow in the interaction with humans. We also
design an integrated open-domain dialogue system containing pre-process,
dialogue model, scoring model, and post-process, which can generate fluent,
coherent, consistent, and humanlike responses. We tie 1st on human ratings and
also get the highest Meteor, and Bert-score in sub-task 1, and rank 3rd on
interactive human evaluation in sub-task 2.

本论文介绍并参加了 DSTC9 互动式对话评估赛中的子任务一和子任务二，提出了预训练语言模型用于生成与特定话题相关的回复，并提出了回复集合方法，同时提出了用于捕获与人类互动中对话流程的对话策划模型，设计了一种集成的开放域对话系统，具有流畅，连贯，连贯和人类特征的响应，并在子任务一中获得了第一名的人类评分和最高的 Meteor 和 Bert 分数，在子任务二中排名第三。