Towards Data-efficient Customer Intent Recognition with Prompt-based Learning Paradigm
This addresses data efficiency in AI-driven customer service, though it appears incremental as it builds on existing prompt-based methods.
The paper tackles the problem of customer intent recognition with limited labeled data by introducing a prompt-based learning paradigm, achieving competitive performance with minimal training data and demonstrating zero-shot potential with detailed prompts.
Recognizing customer intent accurately with language models based on customer-agent conversational data is essential in today's digital customer service marketplace, but it is often hindered by the lack of sufficient labeled data. In this paper, we introduce the prompt-based learning paradigm that significantly reduces the dependency on extensive datasets. Utilizing prompted training combined with answer mapping techniques, this approach allows small language models to achieve competitive intent recognition performance with only a minimal amount of training data. Furthermore, We enhance the performance by integrating active sampling and ensemble learning strategies in the prompted training pipeline. Additionally, preliminary tests in a zero-shot setting demonstrate that, with well-crafted and detailed prompts, small language models show considerable instruction-following potential even without any further training. These results highlight the viability of semantic modeling of conversational data in a more data-efficient manner with minimal data use, paving the way for advancements in AI-driven customer service.