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

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