Interpretable Text Embeddings and Text Similarity Explanation: A Survey
It provides a structured overview for researchers in NLP, though it is incremental as a survey.
The paper surveys methods for interpretable text embeddings and text similarity explanation, addressing challenges in understanding these fundamental NLP components, but does not report specific results or numbers.
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.