Datasets:

inseq
/

scat

	---
	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
	- arxiv:2310.01188
	task_categories:
	- translation
	task_ids: []
	---

	# Dataset Card for SCAT

	## Table of Contents

	- [Dataset Card for SCAT](#dataset-card-for-scat)
	- [Table of Contents](#table-of-contents)
	- [Dataset Description](#dataset-description)
	- [Dataset Summary](#dataset-summary)
	- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
	- [Machine Translation](#machine-translation)
	- [Languages](#languages)
	- [Dataset Structure](#dataset-structure)
	- [Data Instances](#data-instances)
	- [Data Splits](#data-splits)
	- [Dataset Creation](#dataset-creation)
	- [Additional Preprocessing](#additional-preprocessing)
	- [Additional Information](#additional-information)
	- [Dataset Curators](#dataset-curators)
	- [Licensing Information](#licensing-information)
	- [Citation Information](#citation-information)

	## Dataset Description

	- Repository: [Github](https://github.com/neulab/contextual-mt/tree/master/data/scat)
	- Original Paper: [ACL 2021](https://aclanthology.org/2021.acl-long.65/)
	- SCAT+ Paper: [ICLR 2024](https://openreview.net/forum?id=XTHfNGI3zT)
	- SCAT+ Paper: [Arxiv](arxiv.org/abs/2310.01188)
	- Point of Contact SCAT: [Kayo Yin](mailto:kayoyin@berkeley.edu)
	- Point of Contact SCAT+: [Gabriele Sarti](mailto:gabriele.sarti996@gmail.com)

	### 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?"](https://aclanthology.org/2021.acl-long.65/) by Yin et al. (2021), and an original version of the corpus is hosted on [Github](https://github.com/neulab/contextual-mt/tree/master/data/scat) with no licensing information. This dataset contains a curated version of the original corpus (SCAT+, from [Sarti et al. (2024)](https://openreview.net/forum?id=XTHfNGI3zT)) where examples containing nested or malformed tags were removed (refer to the [filter_scat.py](filter_scat.py) script for more details).

	### Supported Tasks and Leaderboards
	#### Machine Translation
	Refer to the [original paper](https://aclanthology.org/2021.acl-long.65/) 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](https://www.aclweb.org/anthology/L18-1275/) corpus and translated into the French (`fr`)
	## Dataset Structure
	### Data Instances

	The dataset contains a single default configuration. Dataset examples have the following format:

	```json
	{
	"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 it 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 plantes , et je leur ai donné de l'eau et du soleil.",
	"fr": "Mais j'ai donné une attention particulière à une pour voir si elle grandit plus.",
	"contrast_fr": "Mais j'ai donné une attention particulière à une pour voir si il grandit plus.",
	"context_en_with_tags": "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_with_tags": "But I gave one special attention to see if <p>it</p> would grow more.",
	"context_fr_with_tags": "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_with_tags": "Mais j'ai donné une attention particulière à une pour voir si <p>elle</p> grandit plus.",
	"contrast_fr_with_tags": "Mais j'ai donné une attention particulière à une pour voir si <p>il</p> grandit plus.",
	"has_supporting_context": true,
	"has_supporting_preceding_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_with_tags` and `fr_with_tags` 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`. Since the context is not in the same sentence as the target pronoun but in a preceding one, `has_supporting_preceding_context` is also true.

	Fields with the `_with_tags` suffix contain tags around pronouns of interest and supporting context, while their counterparts without the suffix contain the same text without tags, to facilitate direct usage with machine translation models.

	### 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 \| 6086 \|
	\| `validation` \| 145 \| 66 \|
	\| `test` \| 1000 \| 500 \|

	### 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?](https://aclanthology.org/2021.acl-long.65/) for additional information on dataset creation.

	### Additional Preprocessing

	Compared to the original SCAT corpus, the following differences are present in this version:

	- Examples were filtered using the [filter_scat.py](filter_scat.py) script to retain only examples containing well-formed tags, and remove superfluous tags. Superfluous tags are defined as nested `<hon><p>...</p><hoff>` tags that represent lack of contextual information for disambiguating the correct pronoun. In this case, the outer `<hon>...<hoff>` tag was removed. We also filter examples that do not contain tags for the pronoun on interest in the target sentence for English and French (`en`, `fr`).

	- Sentences stripped from tags are provided in fields without the `_with_tags` suffix.

	- Tags erroneously embedded within words of target sentence (e.g. `just s<p>it</p>ting there, wa<p>it</p>ing for his face`) were removed.

	- An extra contrastive sentence using the pronoun of interest belonging to the opposite gender is available in the `contrast_fr` field. The swap was performed using a simple lexical heuristic (refer to `swap_pronoun` in [`scat.py`](./scat.py)), and we do not guarantee grammatical correctness of the sentence.

	The test set was manually curated to include 250 examples with supporting context in the previous sentences (`has_supporting_preceding_context` is `true`), and 250 examples with supporting context in the same sentence (`has_supporting_preceding_context` is `false`).

	## 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](mailto: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.

	#### Original SCAT corpus

	```bibtex
	@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",
	}
	```

	#### SCAT+ (This version)

	```bibtex
	@inproceedings{sarti-etal-2023-quantifying,
	title = "Quantifying the Plausibility of Context Reliance in Neural Machine Translation",
	author = "Sarti, Gabriele and
	Chrupa{\l}a, Grzegorz and
	Nissim, Malvina and
	Bisazza, Arianna",
	booktitle = "The Twelfth International Conference on Learning Representations (ICLR 2024)",
	month = may,
	year = "2024",
	address = "Vienna, Austria",
	publisher = "OpenReview",
	url = "https://openreview.net/forum?id=XTHfNGI3zT"
	}
	```