ZurichNLP/rsd-ists-2016
Training and test data for the task of Recognizing Semantic Differences (RSD). See the paper for details on how the dataset was created, and see our code at https://github.com/ZurichNLP/recognizing-semantic-differences for an example of how to use the data for evaluation. The data are derived from the SemEval-2016 Task 2 for Interpretable Semantic Textual Similarity organized by Agirre et al. (2016). The original URLs of the data are: Train:… See the full description on the dataset page: https://huggingface.co/datasets/ZurichNLP/rsd-ists-2016.
Training and test data for the task of Recognizing Semantic Differences (RSD).
See the paper for details on how the dataset was created, and see our code at https://github.com/ZurichNLP/recognizing-semantic-differences for an example of how to use the data for evaluation.
The data are derived from the SemEval-2016 Task 2 for Interpretable Semantic Textual Similarity organized by Agirre et al. (2016). The original URLs of the data are:
- Train: http://alt.qcri.org/semeval2016/task2/data/uploads/train201510_22.utf-8.tar.gz
- Test: http://alt.qcri.org/semeval2016/task2/data/uploads/test_goldstandard.tar.gz
The translations into non-English languages have been created using machine translation (DeepL).
Citation
@inproceedings{vamvas-sennrich-2023-rsd,
title={Towards Unsupervised Recognition of Token-level Semantic Differences in Related Documents},
author={Jannis Vamvas and Rico Sennrich},
month = dec,
year = "2023",
booktitle = "Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing",
address = "Singapore",
publisher = "Association for Computational Linguistics",
}