README.md

metadata

annotations_creators:
  - crowdsourced
  - machine-generated
language_creators:
  - crowdsourced
  - machine-generated
languages:
  - en
  - fr
licenses:
  - mit
multilinguality:
  - translation
size_categories:
  - 1K<n<10K
source_datasets: []
task_categories:
  - text-classification
  - text-scoring
task_ids:
  - semantic-similarity-classification
  - semantic-similarity-scoring

Dataset Card for [Dataset Name]

Table of Contents

• Dataset Description
  • Dataset Summary
  • Supported Tasks
  • Languages
• Dataset Structure
  • Data Instances
  • Data Fields
  • Data Splits
• Dataset Creation
  • Curation Rationale
  • Source Data
  • Annotations
  • Personal and Sensitive Information
• Considerations for Using the Data
  • Social Impact of Dataset
  • Discussion of Biases
  • Other Known Limitations
• Additional Information
  • Dataset Curators
  • Licensing Information
  • Citation Information

Dataset Description

• Homepage: Github
• Repository: Github
• Paper: Aclweb
• Leaderboard:
• Point of Contact:

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.

Supported Tasks and Leaderboards

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.

Dataset Creation

Curation Rationale

See paper.

Source Data

Initial Data Collection and Normalization

[More Information Needed]

Who are the source language producers?

[More Information Needed]

Annotations

Annotation process

See paper.

Who are the annotators?

See paper.

Personal and Sensitive Information

[More Information Needed]

Considerations for Using the Data

Social Impact of Dataset

[More Information Needed]

Discussion of Biases

[More Information Needed]

Other Known Limitations

[More Information Needed]

Additional Information

Dataset Curators

Eleftheria Briakou and Marine Carpuat

Licensing Information

MIT License

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",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.121",
    pages = "1563--1580",
}