# Dataset: refresd

Languages: en fr
Multilinguality: translation
Size Categories: 1K<n<10K

# Dataset Card for [Dataset Name]

### Dataset Summary

The Rationalized English-French Semantic Divergences (REFreSD) dataset consists of 1,039 English-French sentence-pairs annotated with sentence-level divergence judgments and token-level rationales. For any questions, write to ebriakou@cs.umd.edu.

Similarity classification and scoring (3 classes).

### Languages

English and French

## Dataset Structure

### Data Instances

Each data point looks like this:

{
'sentence_pair': {'en': 'The invention of farming some 10,000 years ago led to the development of agrarian societies , whether nomadic or peasant , the latter in particular almost always dominated by a strong sense of traditionalism .',
'fr': "En quelques décennies , l' activité économique de la vallée est passée d' une mono-activité agricole essentiellement vivrière , à une quasi mono-activité touristique , si l' on excepte un artisanat du bâtiment traditionnel important , en partie saisonnier ."}
'label': 0,
'all_labels': 0,
'rationale_en': [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
'rationale_fr': [2, 3, 3, 3, 3, 3, 3, 3, 3, 3, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 3, 3, 3, 3],
}

### Data Fields

• sentence_pair: Dictionary of sentences containing the following field.

• en: The English sentence.
• fr: The corresponding (or not) French sentence.
• label: Binary. Whether both sentences correspond. {0:divergent, 1:equivalent}

• all_labels: 3-class label {0: "unrelated", 1: "some_meaning_difference", 2:"no_meaning_difference"}. The first two are sub-classes of the divergent label.

• rationale_en: Word-aligned rationale for the classification, from English.

• rationale_fr: Word-aligned rationale for the classification, from French.

### Data Splits

1039 sentence pairs in a single "train" split.

See paper.

See paper.

See paper.

## Considerations for Using the Data

### Dataset Curators

Eleftheria Briakou and Marine Carpuat

### Citation Information

@inproceedings{briakou-carpuat-2020-detecting,
title = "Detecting Fine-Grained Cross-Lingual Semantic Divergences without Supervision by Learning to Rank",
author = "Briakou, Eleftheria and Carpuat, Marine",
booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
month = nov,
year = "2020",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/2020.emnlp-main.121",
pages = "1563--1580",
}

### Contributions

Thanks to @mpariente for adding this dataset.

Homepage:
Github
Repository:
Github
Paper:
Aclweb