🤖 AI Summary
This work addresses a critical limitation in existing sentence embedding evaluation methods, which rely on downstream classifiers and thus conflate improvements in embedding quality with classifier-induced biases. To overcome this, the authors propose a classifier-free evaluation framework that quantifies how embeddings respond differently to syntactic noise and semantic negation injected into sentences. They introduce the novel “concept separation curve” to visualize a model’s ability to distinguish surface-level perturbations from genuine semantic changes. The approach is validated across multiple languages (English and Dutch), domains, and sentence lengths, demonstrating its effectiveness in providing an interpretable, reproducible, and model-agnostic assessment of conceptual stability in sentence embeddings. This significantly enhances the reliability and transparency of embedding quality evaluation.
📝 Abstract
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.