一种生成可解释语义文本嵌入的通用框架
A General Framework for Producing Interpretable Semantic Text Embeddings
- National University of Singapore（新加坡国立大学）
- Harbin Institute of Technology (Shenzhen)（哈尔滨工业大学（深圳））
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
针对现有可解释语义文本嵌入方法依赖专家输入或提示设计、泛化性不足的问题，提出CQG-MBQA通用框架，通过对比式问题生成和多任务二元问答生成可解释嵌入，性能媲美先进黑盒模型且优于同类可解释方法。
AI中文摘要：
语义文本嵌入是自然语言处理（NLP）中众多任务的核心要素。尽管黑盒模型能够生成高质量的嵌入，但其可解释性的缺失限制了它们在要求透明度的任务中的应用。近期的方法通过利用领域专家设计或大语言模型（LLM）生成的问题提升了可解释性，但这些方法严重依赖专家输入或精心的提示设计，这限制了它们的泛化能力以及在广泛任务中生成判别性问题的能力。为应对这些挑战，我们提出了CQG-MBQA（对比式问题生成-多任务二元问题回答），这是一个适用于多种任务的生成可解释语义文本嵌入的通用框架。我们的框架通过CQG方法系统地生成高判别性、低认知负荷的是/否问题，并通过MBQA模型高效回答这些问题，以高成本效益的方式生成可解释的嵌入。我们通过大量实验和消融研究验证了CQG-MBQA的有效性和可解释性，证明其在保持固有可解释性的同时，嵌入质量可与许多先进的黑盒模型相媲美。此外，CQG-MBQA在各类下游任务上的表现优于其他可解释文本嵌入方法。
英文摘要：
Semantic text embedding is essential to many tasks in Natural Language Processing (NLP). While black-box models are capable of generating high-quality embeddings, their lack of interpretability limits their use in tasks that demand transparency. Recent approaches have improved interpretability by leveraging domain-expert-crafted or LLM-generated questions, but these methods rely heavily on expert input or well-prompt design, which restricts their generalizability and ability to generate discriminative questions across a wide range of tasks. To address these challenges, we introduce \algo{CQG-MBQA} (Contrastive Question Generation - Multi-task Binary Question Answering), a general framework for producing interpretable semantic text embeddings across diverse tasks. Our framework systematically generates highly discriminative, low cognitive load yes/no questions through the \algo{CQG} method and answers them efficiently with the \algo{MBQA} model, resulting in interpretable embeddings in a cost-effective manner. We validate the effectiveness and interpretability of \algo{CQG-MBQA} through extensive experiments and ablation studies, demonstrating that it delivers embedding quality comparable to many advanced black-box models while maintaining inherently interpretability. Additionally, \algo{CQG-MBQA} outperforms other interpretable text embedding methods across various downstream tasks.
