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。