解析文本嵌入与相似性解释的可读方法，让模型决策更透明。
Interpretable Text Embeddings and Text Similarity Explanation: A Survey
- 系统梳理可解释文本嵌入的核心思路与技术路径
- 对比评估不同方法在解释效果上的优劣与权衡
- 适合关注模型可解释性与NLP透明度的研究者
文本嵌入是分类、回归、聚类和语义搜索等众多自然语言处理任务的基础。尽管应用广泛，但对嵌入的解释以及它们之间相似性的说明仍面临挑战。本文系统综述了专注于内在可解释文本嵌入与文本相似性解释的方法，这一研究方向尚不充分。我们归纳了主要思想、技术路线与权衡关系，比较了评估手段，总结了关键经验，并指出了未来研究的机会与开放性挑战。
原文摘要 · Abstract (English)
Text embeddings are a fundamental component in many NLP tasks, including classification, regression, clustering, and semantic search. However, despite their ubiquitous application, challenges persist in interpreting embeddings and explaining similarities between them. In this work, we provide a structured overview of methods specializing in inherently interpretable text embeddings and text similarity explanation, an underexplored research area. We characterize the main ideas, approaches, and trade-offs. We compare means of evaluation, discuss overarching lessons learned and finally identify opportunities and open challenges for future research.
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