
Training and test data for the task of Recognizing Semantic Differences (RSD).

[See the paper](https://arxiv.org/abs/2305.13303) 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](https://alt.qcri.org/semeval2016/task2/) organized by [Agirre et al. (2016)](http://dx.doi.org/10.18653/v1/S16-1082).
The original URLs of the data are:
* Train: http://alt.qcri.org/semeval2016/task2/data/uploads/train_2015_10_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
```bibtex
@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",
}
```

task_categories:token-classification language_creators:machine-generated size_categories:1K<n<10K language:en language:de language:es language:fr language:ja language:ko language:zh license:cc-by-sa-4.0 arxiv:2305.13303 region:us
{"language_creators": ["machine-generated"], "language": ["en", "de", "es", "fr", "ja", "ko", "zh"], "license": "cc-by-sa-4.0", "size_categories": ["1K<n<10K"], "task_categories": ["token-classification"], "dataset_info": {"features": [{"name": "tokens_a", "sequence": "string"}, {"name": "tokens_b", "sequence": "string"}, {"name": "labels_a", "sequence": "float64"}, {"name": "labels_b", "sequence": "float64"}, {"name": "lang_a", "dtype": "string"}, {"name": "lang_b", "dtype": "string"}, {"name": "subset", "dtype": "string"}, {"name": "id", "dtype": "string"}, {"name": "alignments", "dtype": "string"}], "splits": [{"name": "train_en", "num_bytes": 1640900, "num_examples": 1506}, {"name": "train_de", "num_bytes": 1101404, "num_examples": 3012}, {"name": "train_es", "num_bytes": 1154765, "num_examples": 3012}, {"name": "train_fr", "num_bytes": 1206414, "num_examples": 3012}, {"name": "train_ja", "num_bytes": 838252, "num_examples": 3012}, {"name": "train_ko", "num_bytes": 829328, "num_examples": 3012}, {"name": "train_zh", "num_bytes": 796140, "num_examples": 3012}, {"name": "test_en", "num_bytes": 833900, "num_examples": 750}, {"name": "test_de", "num_bytes": 558624, "num_examples": 1500}, {"name": "test_es", "num_bytes": 580224, "num_examples": 1500}, {"name": "test_fr", "num_bytes": 610017, "num_examples": 1500}, {"name": "test_ja", "num_bytes": 425912, "num_examples": 1500}, {"name": "test_ko", "num_bytes": 424407, "num_examples": 1500}, {"name": "test_zh", "num_bytes": 403680, "num_examples": 1500}], "download_size": 2569205, "dataset_size": 11403967}}
TAGS
#task_categories-token-classification #language_creators-machine-generated #size_categories-1K<n<10K #language-English #language-German #language-Spanish #language-French #language-Japanese #language-Korean #language-Chinese #license-cc-by-sa-4.0 #arxiv-2305.13303 #region-us 


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 URL 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: URL
* Test: URL

The translations into non-English languages have been created using machine translation (DeepL).


TAGS
#task_categories-token-classification #language_creators-machine-generated #size_categories-1K<n<10K #language-English #language-German #language-Spanish #language-French #language-Japanese #language-Korean #language-Chinese #license-cc-by-sa-4.0 #arxiv-2305.13303 #region-us 
