Datasets:
File size: 2,189 Bytes
c4f878a 65afebe c4f878a 65afebe | 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 | ---
dataset_info:
features:
- name: data
dtype: string
- name: text
dtype: string
splits:
- name: train
num_bytes: 8160924
num_examples: 33525
download_size: 2382711
dataset_size: 8160924
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
language:
- fra
license: cc-by-sa-4.0
task_categories:
- table-to-text
---
## Description
French translation of the [E2E NLG](https://huggingface.co/datasets/GEM/e2e_nlg) dataset.
According of the English version dataset card, The E2E NLG dataset is a benchmark dataset for data-to-text models that verbalize a set of 2-9 key-value attribute pairs in the restaurant domain. The version used for GEM is the cleaned E2E NLG dataset, which filters examples with hallucinations and outputs that don't fully cover all input attributes.
You can find the main data card on the [GEM Website](https://gem-benchmark.com/data_cards/e2e_nlg) and we invite you to consult the paper in the below citation part.
## Citation
```
@inproceedings{dusek-etal-2019-semantic,
title = "Semantic Noise Matters for Neural Natural Language Generation",
author = "Du{\v{s}}ek, Ond{\v{r}}ej and
Howcroft, David M. and
Rieser, Verena",
editor = "van Deemter, Kees and
Lin, Chenghua and
Takamura, Hiroya",
booktitle = "Proceedings of the 12th International Conference on Natural Language Generation",
month = oct # "–" # nov,
year = "2019",
address = "Tokyo, Japan",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W19-8652/",
doi = "10.18653/v1/W19-8652",
pages = "421--426",
abstract = "Neural natural language generation (NNLG) systems are known for their pathological outputs, i.e. generating text which is unrelated to the input specification. In this paper, we show the impact of semantic noise on state-of-the-art NNLG models which implement different semantic control mechanisms. We find that cleaned data can improve semantic correctness by up to 97{\%}, while maintaining fluency. We also find that the most common error is omitting information, rather than hallucination."
}
``` |