Datasets:
annotations_creators:
- expert-generated
language:
- en
- fr
language_creators:
- expert-generated
license:
- unknown
multilinguality:
- translation
pretty_name: scat
size_categories:
- 10K<n<100K
source_datasets:
- original
tags:
- contextual mt
- document mt
task_categories:
- translation
task_ids: []
dataset_info:
features:
- name: id
dtype: int32
- name: context_en
dtype: string
- name: en
dtype: string
- name: context_fr
dtype: string
- name: fr
dtype: string
- name: has_supporting_context
dtype: bool
config_name: sentences
splits:
- name: train
num_bytes: 6813668
num_examples: 11144
- name: validation
num_bytes: 93178
num_examples: 144
- name: test
num_bytes: 583831
num_examples: 973
download_size: 7292968
dataset_size: 7490677
Dataset Card for SCAT
Table of Contents
Dataset Description
Dataset Summary
The Supporting Context for Ambiguous Translations corpus (SCAT) is a dataset of English-to-French translations annotated with human rationales used for resolving ambiguity in pronoun anaphora resolution for multi-sentence translation.
Disclaimer: The SCAT corpus was released in the ACL 2021 paper "Do Context-Aware Translation Models Pay the Right Attention?" by Yin et al. (2021), and an original version of the corpus is hosted on Github with no licensing information. This dataset contains a curated version of the original corpus where examples containing nested or malformed tags were removed (refer to the filter_scat.py script for more details).
Supported Tasks and Leaderboards
Machine Translation
Refer to the original paper for additional details on plausibility evaluation for document-level MT systems.
Languages
The dataset contains source English sentences containing anaphoric pronouns selected from the OpenSubtitles 2018 corpus and translated into the French (fr)
Dataset Structure
Data Instances
The dataset contains a single default configuration. Dataset examples have the following format:
{
"id": 0,
"context_en": "Air, water, the continents. So, what is your project about and what are its chances of winning? - Well, my project is awesome. - Oh, good. I took two plants, and I gave them sun and water",
"en": "But I gave one special attention to see if <p>it</p> would grow more.",
"context_fr": "L'air, l'eau, les continents. Donc, quel est le sujet de ton projet et quelles sont ses chances de gagner ? - Bien, mon projet est impressionnant. - Oh, bien. J'ai pris deux <hon>plantes<hoff> , et je leur ai donné de l'eau et du soleil.",
"fr": "Mais j'ai donné une attention particulière à une pour voir si <p>elle</p> grandit plus.",
"has_supporting_context": true,
}
In every example, the pronoun of interest and its translation are surrounded by <p>...</p> tags. These are guaranteed to be found in the en and fr field, respectively.
Any span surrounded by <hon>...<hoff> tags was identified by human annotators as supporting context to correctly translate the pronoun of interest. These spans can be missing altogether (i.e. no contextual information needed), or they can be found in any of the available fields. The has_supporting_context field indicates whether the example contains any supporting context.
In the example above, the translation of the pronoun it (field en) is ambiguous, and the correct translation to the feminine French pronoun elle (in field fr) is only possible thanks to the supporting feminine noun plantes in the field context_fr. Since the example contains supporting context, the has_supporting_context field is set to true.
Data Splits
The dataset is split into train, validation and test sets. In the following table, we report the number of examples in the original dataset and in this filtered version in which examples containing malformed tags were removed.
| Split | # Examples (original) | # Examples (this) |
|---|---|---|
train |
11471 | 11144 |
validation |
145 | 144 |
test |
1000 | 973 |
Dataset Creation
From the original paper:
We recruited 20 freelance English-French translators on Upwork. We annotate examples from the contrastive test set by Lopes et al. (2020). This set includes 14K examples from the OpenSubtitles2018 dataset. Through our annotation effort, we obtain 14K examples of supporting context for pronoun anaphora resolution in ambiguous translations selected by professional human translators.
Please refer to the original article Do Context-Aware Translation Models Pay the Right Attention? for additional information on dataset creation.
Additional Information
Dataset Curators
The original authors of SCAT are the curators of the original released dataset. For problems or updates on this 🤗 Datasets version, please contact gabriele.sarti996@gmail.com.
Licensing Information
The dataset license is unknown.
Citation Information
Please cite the authors if you use these corpus in your work.
@inproceedings{yin-etal-2021-context,
title = "Do Context-Aware Translation Models Pay the Right Attention?",
author = "Yin, Kayo and
Fernandes, Patrick and
Pruthi, Danish and
Chaudhary, Aditi and
Martins, Andr{\'e} F. T. and
Neubig, Graham",
booktitle = "Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)",
month = aug,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.acl-long.65",
doi = "10.18653/v1/2021.acl-long.65",
pages = "788--801",
}