This paper discusses our approaches for task-oriented conversational
modelling using subjective knowledge, with a particular emphasis on response
generation. Our methodology was shaped by an extensive data analysis that
evaluated key factors such as response length, sentiment, and dialogue acts
present in the provided dataset. We used few-shot learning to augment the data
with newly generated subjective knowledge items and present three approaches
for DSTC11: (1) task-specific model exploration, (2) incorporation of the most
frequent question into all generated responses, and (3) a waterfall prompting
technique using a combination of both GPT-3 and ChatGPT.

本文讨论使用主观知识进行任务导向型对话建模的方法，重点关注回答生成。我们通过广泛的数据分析确定了回答长度、情感和对话行为等关键因素，并使用少样本学习来增强数据。我们提出了三种方法应用于 DSTC11：（1）任务特定模型探索，（2）将最频繁的问题纳入所有生成的回答，以及 （3）结合了 GPT-3 和 ChatGPT 的瀑布提示技术。

利用少样本数据增强和逐层引导的响应生成

Leveraging Few-Shot Data Augmentation and Waterfall Prompting for  Response Generation

The advent and fast development of neural networks have revolutionized the
research on dialogue systems and subsequently have triggered various challenges
regarding their automatic evaluation. Automatic evaluation of open-domain
dialogue systems as an open challenge has been the center of the attention of
many researchers. Despite the consistent efforts to improve automatic metrics'
correlations with human evaluation, there have been very few attempts to assess
their robustness over multiple domains and dimensions. Also, their focus is
mainly on the English language. All of these challenges prompt the development
of automatic evaluation metrics that are reliable in various domains,
dimensions, and languages. This track in the 11th Dialogue System Technology
Challenge (DSTC11) is part of the ongoing effort to promote robust and
multilingual automatic evaluation metrics. This article describes the datasets
and baselines provided to participants and discusses the submission and result
details of the two proposed subtasks.

本文提出神经网络和自动评估方法在对话系统中的应用，特别关注 DSTC11 的自动评估度量标准和结果。