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---
dataset_info:
features:
- name: Utterance
dtype: string
- name: politeness
dtype: float64
- name: language
dtype: string
- name: __index_level_0__
dtype: int64
splits:
- name: train
num_bytes: 4954742
num_examples: 18238
- name: validation
num_bytes: 619659
num_examples: 2280
- name: test
num_bytes: 619627
num_examples: 2280
download_size: 3919716
dataset_size: 6194028
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
- split: test
path: data/test-*
---
# Dataset Card for "multilingual_politeness"
This dataset has the following attributes:
- Utterance: a 2-3 sentence excerpt from Wikipedia editor talk pages.
- language: English, Spanish, Chinese, or Japanese
- politeness: An annotated politeness label ranging from -2 (very rude) to 2 (very polite). Each utterance was annotated by 3 native speakers.
Please cite the following paper if you use our dataset :)
```
@inproceedings{havaldar-etal-2023-comparing,
title = "Comparing Styles across Languages",
author = "Havaldar, Shreya and Pressimone, Matthew and Wong, Eric and Ungar, Lyle",
booktitle = "Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing",
year = "2023",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.emnlp-main.419"
}
``` |