Abstract: Low-resource scenarios in natural language processing often face the challenge of data scarcity, which is particularly prominent in the field of lighting intelligent customer service. This domain is characterized by dense professional terminologies, sparse specification parameters, and redundant combinations of services and products, making it difficult to obtain high-quality annotated intent data. Existing mainstream data augmentation methods, such as synonym replacement and back-translation, struggle to improve semantic diversity or resolve ambiguous intent boundaries, while generative augmentation methods based on large language models are prone to issues such as intent drift and entity errors, with insufficient domain adaptability. To address these problems, this paper proposes a Guided Constraint Deduplication Data Augmentation method (GCDDA). This method constructs a three-stage collaborative framework consisting of few-shot example guidance, multi-dimensional constraint filtering, and semantic similarity deduplication. Specifically, few-shot example guidance uses a small number of high-quality seed samples as references to enable rapid adaptation of the model to the generation paradigm in the lighting domain, compensating for the lack of large-scale manual annotation. Multi-dimensional constraints impose fine-grained control over the generation process at three levels—intent, entity, and scenario language—effectively suppressing common intent drift and entity violations in generative augmentation. Semantic similarity deduplication employs an LLM-driven duplication scoring mechanism to forcibly eliminate redundant samples that exhibit "same logic, different wording," fundamentally mitigating the deficiencies of traditional methods in semantic diversity and ambiguous intent boundaries. Through the synergy of these three components, the method can generate quality-controllable, low-redundancy intent samples in batches without fine-tuning the generative language model. Experimental results demonstrate that on the self-built Light-Intent dataset, with BERT-base-Chinese as the downstream classification model, the accuracy of the 5-shot baseline is only 40.54%, which increases to 63.34% after GCDDA augmentation, while the F1 score improves from 40.33% to 63.66%. Moreover, the augmented performance (5+15-shot) outperforms the same-scale real data (20-shot) with 60.48% accuracy and 60.88% F1. On three public datasets, ATIS, Kuake-qic, and a_intent, GCDDA consistently outperforms traditional augmentation methods such as back-translation and synonym replacement, as well as generative methods including ZeroGen, AugGPT, and PromptMix under the same sample size conditions. The proposed method requires no fine-tuning of large language models and can be flexibly transferred.

摘要： 自然语言处理中的低资源场景常面临数据稀缺挑战，照明智能客服领域尤为突出，其专业术语密集、规格参数稀疏、服务与产品组合冗余，导致高质量意图标注数据获取困难。现有同义词替换、回译等传统增强方法难以提升语义多样性、化解意图边界模糊；基于大语言模型的生成式增强则易出现意图偏移、实体错误等问题，领域适配性不足。针对上述问题，本文提出引导式约束去重数据增强方法（Guided Constraint Deduplication Data Augmentation, GCDDA）。该方法构建了少样本示例引导、多维度约束筛选与语义相似度去重的三阶协同框架：少样本示例引导以少量高质量种子样本为参照，使模型快速适配照明领域的生成范式，弥补大规模人工标注的缺失；多维度约束从意图、实体及场景语言三个层次对生成过程施加细粒度控制，有效抑制生成式增强中常见的意图偏移与实体违规；语义相似度去重通过大语言模型驱动的重复度打分机制，强制剔除“换词不换逻辑”的冗余样本，从根本上化解传统方法语义多样性不足与意图边界模糊的缺陷。三者协同，在无需对生成式语言模型进行微调的前提下，即可批量生成质量可控、低冗余的意图样本。实验结果表明，在自建Light-Intent数据集上，以BERT-base-Chinese为下游分类模型，（5-shot）基线的准确率仅为40.54%，经GCDDA增强后达到63.34%，F1值从40.33%提升至63.66%，且增强后的性能（5+15-shot）优于同规模真实数据（20-shot）的60.48%准确率和60.88%F1值；在ATIS、Kuake-qic、a_intent三个公开数据集上，GCDDA在同等样本规模下优于回译、同义词替换等传统增强方法及ZeroGen、AugGPT及PromptMix等生成式增强方法。该方法无需对生成式大语言模型进行微调，可灵活迁移。