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.

翻译：本文探讨了我们在利用主观知识进行任务导向型对话建模中的方法，重点聚焦于回复生成。我们的方法论建立在广泛的数据分析基础之上，评估了所提供数据集中的关键因素，如回复长度、情感以及对话行为。我们采用少样本学习，通过新生成的主观知识项来增强数据，并针对DSTC11提出了三种方法：(1) 任务特定模型探索，(2) 将所有生成的回复中融入最常见的问题，以及(3) 一种结合GPT-3与ChatGPT的瀑布式提示技术。