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Interpretable Text Embeddings and Text Similarity Explanation: A Survey

Created by
  • Haebom

Author

Juri Opitz, Lucas Moller, Andrianos Michail, Sebastian Pad o, Simon Clematide

Outline

Text embeddings are a core component of many NLP tasks, such as classification, regression, clustering, and semantic retrieval. This paper systematically outlines methods specializing in the interpretation and similarity description of text embeddings, characterizing key ideas, approaches, and tradeoffs in this research field. It also compares evaluation methods, discusses overall lessons learned, and identifies opportunities and challenges for future research.

Takeaways, Limitations

Provides a structured overview of interpretable text embedding and text similarity explanation methodologies.
Characterization of key ideas, approaches, and trade-offs.
Comparison of evaluation methods and discussion of overall lessons learned.
Identifying opportunities and challenges for future research.
Lack of details on specific methodologies focusing on interpretability.
It may not be possible to comprehensively cover all potential avenues in the field.
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