LivNLP/C-STS-Reannotated

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@inproceedings{zhang-etal-2025-annotating,
    title = "Annotating Training Data for Conditional Semantic Textual Similarity Measurement using Large Language Models",
    author = "Zhang, Gaifan  and
      Zhou, Yi  and
      Bollegala, Danushka",
    editor = "Christodoulopoulos, Christos  and
      Chakraborty, Tanmoy  and
      Rose, Carolyn  and
      Peng, Violet",
    booktitle = "Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2025",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2025.emnlp-main.1373/",
    doi = "10.18653/v1/2025.emnlp-main.1373",
    pages = "27015--27027",
    ISBN = "979-8-89176-332-6",
    abstract = "Semantic similarity between two sentences depends on the aspects considered between those sentences. To study this phenomenon, Deshpande et al. (2023) proposed the Conditional Semantic Textual Similarity (C-STS) task and annotated a human-rated similarity dataset containing pairs of sentences compared under two different conditions. However, Tu et al. (2024) found various annotation issues in this dataset and showed that manually re-annotating a small portion of it leads to more accurate C-STS models. Despite these pioneering efforts, the lack of large and accurately annotated C-STS datasets remains a blocker for making progress on this task as evidenced by the subpar performance of the C-STS models. T

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dataset_licence({ "id": "LivNLP/C-STS-Reannotated" })
signal: "permissive"
stated_licence: "cc-by-4.0"
sources: 0