Datasets:

gsarti
/

magpie

	---
	annotations_creators:
	- expert-generated
	language_creators:
	- expert-generated
	language:
	- en
	license:
	- cc-by-4.0
	multilinguality:
	- monolingual
	size_categories:
	- 10K<n<100K
	source_datasets:
	- original
	task_categories:
	- text-classification
	- text2text-generation
	- translation
	task_ids: []
	pretty_name: magpie
	tags:
	- idiomaticity-classification
	---

	# Dataset Card for MAGPIE

	## Table of Contents

	- [Dataset Card for MAGPIE](#dataset-card-for-itacola)
	- [Table of Contents](#table-of-contents)
	- [Dataset Description](#dataset-description)
	- [Dataset Summary](#dataset-summary)
	- [Languages](#languages)
	- [Dataset Structure](#dataset-structure)
	- [Data Instances](#data-instances)
	- [Data Splits](#data-splits)
	- [Dataset Creation](#dataset-creation)
	- [Additional Information](#additional-information)
	- [Dataset Curators](#dataset-curators)
	- [Licensing Information](#licensing-information)
	- [Citation Information](#citation-information)

	## Dataset Description

	- Original Repository: [hslh/magpie-corpus](https://github.com/hslh/magpie-corpus)
	- Other Repository: [vernadankers/mt_idioms](https://github.com/vernadankers/mt_idioms)
	- Original Paper: [ACL Anthology](https://aclanthology.org/2020.lrec-1.35/)
	- Other Paper: [ACL Anthology](https://aclanthology.org/2022.acl-long.252/)
	- Point of Contact: [Hessel Haagsma, Verna Dankers](vernadankers@gmail.com)

	### Dataset Summary

	The MAGPIE corpus ([Haagsma et al. 2020](https://aclanthology.org/2020.lrec-1.35/)) is a large sense-annotated corpus of potentially idiomatic expressions (PIEs), based on the British National Corpus (BNC). Potentially idiomatic expressions are like idiomatic expressions, but the term also covers literal uses of idiomatic expressions, such as 'I leave work at the end of the day.' for the idiom 'at the end of the day'. This version of the dataset reflects the filtered subset used by [Dankers et al. (2022)](https://aclanthology.org/2022.acl-long.252/) in their investigation on how PIEs are represented by NMT models. Authors use 37k samples annotated as fully figurative or literal, for 1482 idioms that contain nouns, numerals or adjectives that are colors (which they refer to as keywords). Because idioms show syntactic and morphological variability, the focus is mostly put on nouns. PIEs and their context are separated using the original corpus’s word-level annotations.

	### Languages

	The language data in MAGPIE is in English (BCP-47 `en`)

	## Dataset Structure

	### Data Instances

	The `magpie` configuration contains sentences with annotations for the presence, usage an type of potentially idiomatic expressions. An example from the `train` split of the `magpie` config (default) is provided below.

	```json
	{
	'sentence': 'There seems to be a dearth of good small tools across the board.',
	'annotation': [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1],
	'idiom': 'across the board',
	'usage': 'figurative',
	'variant': 'identical',
	'pos_tags': ['ADV', 'VERB', 'PART', 'VERB', 'DET', 'NOUN', 'ADP', 'ADJ', 'ADJ', 'NOUN', 'ADP', 'DET', 'NOUN']
	}
	```

	The text is provided as-is, without further preprocessing or tokenization.

	The fields are the following:

	- `sentence`: The sentence containing a PIE.
	- `annotation`: List of 0s and 1s of the same length of the whitespace-tokenized sentence, with 1s corresponding to the position of the idiomatic expression.
	- `idiom`: The idiom contained in the sentence in its base form.
	- `usage`: Either `figurative` or `literal`, depending on the usage of the PIE.
	- `variant`: `identical` if the PIE matches the base form of the idiom, otherwise specifies the variation.
	- `pos_tags`: List of POS tags associated with words in the sentence.

	### Data Splits

	\| config\| train\|
	\|----------:\|-----:\|
	\|`magpie` \| 44451 \|

	### Dataset Creation

	Please refer to the original article [MAGPIE: A Large Corpus of Potentially Idiomatic Expressions](https://aclanthology.org/2020.lrec-1.35) for additional information on dataset creation, and to the article [Can Transformer be Too Compositional? Analysing Idiom Processing in Neural Machine Translation](https://aclanthology.org/2022.acl-long.252) for further information on the filtering of selected idioms.

	## Additional Information

	### Dataset Curators

	The original authors are the curators of the original dataset. For problems or updates on this 🤗 Datasets version, please contact [gabriele.sarti996@gmail.com](mailto:gabriele.sarti996@gmail.com).

	### Licensing Information

	The dataset is licensed under [Creative Commons 4.0 license (CC-BY-4.0)](https://creativecommons.org/licenses/by/4.0/)

	### Citation Information

	Please cite the authors if you use this corpus in your work:

	```bibtex
	@inproceedings{haagsma-etal-2020-magpie,
	title = "{MAGPIE}: A Large Corpus of Potentially Idiomatic Expressions",
	author = "Haagsma, Hessel and
	Bos, Johan and
	Nissim, Malvina",
	booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
	month = may,
	year = "2020",
	address = "Marseille, France",
	publisher = "European Language Resources Association",
	url = "https://aclanthology.org/2020.lrec-1.35",
	pages = "279--287",
	language = "English",
	ISBN = "979-10-95546-34-4",
	}
	@inproceedings{dankers-etal-2022-transformer,
	title = "Can Transformer be Too Compositional? Analysing Idiom Processing in Neural Machine Translation",
	author = "Dankers, Verna and
	Lucas, Christopher and
	Titov, Ivan",
	booktitle = "Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
	month = may,
	year = "2022",
	address = "Dublin, Ireland",
	publisher = "Association for Computational Linguistics",
	url = "https://aclanthology.org/2022.acl-long.252",
	doi = "10.18653/v1/2022.acl-long.252",
	pages = "3608--3626",
	}
	```