Finding Meaning in Embeddings: Concept Separation Curves
This work addresses the need for objective, classifier-free evaluation of sentence embeddings, which is important for researchers developing or selecting embedding methods.
The authors propose Concept Separation Curves, a classifier-independent method to evaluate how well sentence embeddings capture meaning by measuring their response to syntactic noise and semantic negations. They demonstrate that this approach provides interpretable and reproducible evaluation across models, languages, and domains.
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 syntactic noise and semantic negations into sentences, with the subsequent quantification of their relative effects on the resulting embeddings. The visualisation of these effects is facilitated by Concept Separation Curves, which show the model's capacity to differentiate between conceptual and surface-level variations. By leveraging data from multiple domains, employing both Dutch and English languages, and examining sentence lengths, this study offers a compelling demonstration that Concept Separation Curves provide an interpretable, reproducible, and cross-model approach for evaluating the conceptual stability of sentence embeddings.