3 accepted papers
This work investigates capturing and modeling disagreement in Semantic Textual Similarity (STS), where sentence pairs are assigned ordinal similarity labels (0–5). Conventional STS systems average multiple annotator scores and focus on a single numeric estimate, overlooking label dispersion. By leve
Previous work has reported both empirical and theoretical evidence, for specific training models, of the correspondence between the squared norm of an embedding and the information content of the text it represents.In this paper, we investigate the relationship at the theoretical and empirical level
NLP research frequently grapples with multiple sources of variability—spanning runs, datasets, annotators, and more—yet conventional analysis methods often neglect these hierarchical structures, threatening the reproducibility of findings. To address this gap, we contribute a case study illustrating…