论文标题
预先培训和基于注意力的神经网络，用于建立NOTECITIOT任务对话系统
Pre-Trained and Attention-Based Neural Networks for Building Noetic Task-Oriented Dialogue Systems
论文作者
论文摘要
Noesis II挑战是第8对话系统技术挑战（DSTC 8）的曲目2，是DSTC 7的扩展。此轨道结合了新的元素，这些元素对于创建部署的任务对话系统至关重要。本文描述了我们的系统在此挑战下在所有子任务上进行了评估。我们研究了采用预先训练的基于注意力的网络进行多转化对话系统的问题。同时，提出了几种适应方法，以适应多转向对话系统的预训练的语言模型，以保持对话系统的内在属性。在DSTC 8的轨道2的发布评估结果中，我们提出的模型在子任务1中排名第四，在子任务2中排名第三，在子任务3和子任务4和子任务4中排名第三。
The NOESIS II challenge, as the Track 2 of the 8th Dialogue System Technology Challenges (DSTC 8), is the extension of DSTC 7. This track incorporates new elements that are vital for the creation of a deployed task-oriented dialogue system. This paper describes our systems that are evaluated on all subtasks under this challenge. We study the problem of employing pre-trained attention-based network for multi-turn dialogue systems. Meanwhile, several adaptation methods are proposed to adapt the pre-trained language models for multi-turn dialogue systems, in order to keep the intrinsic property of dialogue systems. In the released evaluation results of Track 2 of DSTC 8, our proposed models ranked fourth in subtask 1, third in subtask 2, and first in subtask 3 and subtask 4 respectively.
