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 "id": "LivNLP/C-STS-Reannotated",
 "owner": "LivNLP",
 "name": "C-STS-Reannotated",
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 "last_modified": "2026-01-22T11:34:08.000Z",
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 "citation": "@inproceedings{zhang-etal-2025-annotating,\n    title = \"Annotating Training Data for Conditional Semantic Textual Similarity Measurement using Large Language Models\",\n    author = \"Zhang, Gaifan  and\n      Zhou, Yi  and\n      Bollegala, Danushka\",\n    editor = \"Christodoulopoulos, Christos  and\n      Chakraborty, Tanmoy  and\n      Rose, Carolyn  and\n      Peng, Violet\",\n    booktitle = \"Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing\",\n    month = nov,\n    year = \"2025\",\n    address = \"Suzhou, China\",\n    publisher = \"Association for Computational Linguistics\",\n    url = \"https://aclanthology.org/2025.emnlp-main.1373/\",\n    doi = \"10.18653/v1/2025.emnlp-main.1373\",\n    pages = \"27015--27027\",\n    ISBN = \"979-8-89176-332-6\",\n    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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