How DDAIR you? Disambiguated Data Augmentation for Intent Recognition

2026-01-16

AI Summary

This work addresses the degradation of classification performance in low-resource intent recognition tasks caused by semantically ambiguous samples generated by large language models. To mitigate this issue, the authors propose the DDAIR framework, which introduces a semantic similarity mechanism based on Sentence Transformers to detect and filter cross-category ambiguous samples. Furthermore, an iterative regeneration strategy is employed to enhance the class discriminability of the synthesized data. By explicitly resolving intent boundary ambiguity, the proposed approach significantly improves intent recognition accuracy under low-resource conditions.

Technology Category

• Natural Language Processing: Generation
• Machine Learning: Large Multimodal Models (LMMs)
• Humans and AI: AI for Accessibility

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Large language models for search
• Web Mining and Content Analysis: Large pretrained models with web data

Abstract

Large Language Models (LLMs) are effective for data augmentation in classification tasks like intent detection. In some cases, they inadvertently produce examples that are ambiguous with regard to untargeted classes. We present DDAIR (Disambiguated Data Augmentation for Intent Recognition) to mitigate this problem. We use Sentence Transformers to detect ambiguous class-guided augmented examples generated by LLMs for intent recognition in low-resource scenarios. We identify synthetic examples that are semantically more similar to another intent than to their target one. We also provide an iterative re-generation method to mitigate such ambiguities. Our findings show that sentence embeddings effectively help to (re)generate less ambiguous examples, and suggest promising potential to improve classification performance in scenarios where intents are loosely or broadly defined.

Problem

• data augmentation
• intent recognition
• semantic ambiguity
• large language models
• low-resource scenarios

Innovation

• Disambiguated Data Augmentation
• Intent Recognition
• Large Language Models
• Sentence Transformers
• Low-resource Classification