Datasets:
Tasks:
Token Classification
Size Categories:
1K<n<10K
Language Creators:
machine-generated
ArXiv:
Tags:
License:
File size: 2,797 Bytes
b0d9a35 bee19bb 32c46b5 56fb389 bee19bb 98251ec e0c1dd2 98251ec 5611211 4f58d5e 5611211 4f58d5e 5611211 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 |
---
license: cc-by-sa-4.0
language_creators:
- machine-generated
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
task_categories:
- token-classification
language:
- en
- de
- es
- fr
- ja
- ko
- zh
size_categories:
- 1K<n<10K
---
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",
}
```
|