Towards Data-efficient Customer Intent Recognition with Prompt-based Learning Paradigm

TL;DR

This paper presents a prompt-based learning approach that enables small language models to recognize customer intent efficiently with minimal labeled data, leveraging prompt engineering, active sampling, and ensemble techniques.

Contribution

It introduces a novel prompt-based paradigm combined with active sampling and ensemble methods to improve intent recognition with limited data.

Findings

Competitive performance with minimal training data

Effective zero-shot intent recognition with detailed prompts

Enhanced accuracy through ensemble learning

Abstract

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.