Finding Meaning in Embeddings: Concept Separation Curves
Sentence embedding techniques aim to encode key concepts of a sentence's meaning in a vector space. However, the majority of evaluation approaches for sentence embedding quality rely on the use of additional classifiers or downstream tasks. These additional components make it unclear whether good results stem from the embedding itself or from the classifier's behaviour. In this paper, we propose a novel method for evaluating the effectiveness of sentence embedding methods in capturing sentence-level concepts. Our approach is classifier-independent, allowing for an objective assessment of the model's performance. The approach adopted in this study involves the systematic introduction of sy...
| Authors | Paul Keuren, Marc Ponsen, Robert Ayoub Bagheri |
|---|---|
| Primary category | cs.CL |
| Published | 23 April 2026 |
| Last updated at source | 23 April 2026 |
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