Datasets:

NYTK
/

HuSST

	---
	annotations_creators:
	- found
	language_creators:
	- found
	- expert-generated
	language:
	- hu
	license:
	- bsd-2-clause
	multilinguality:
	- monolingual
	size_categories:
	- unknown
	source_datasets:
	- extended\|other
	task_categories:
	- text-classification
	task_ids:
	- sentiment-classification
	- sentiment-scoring
	- text-scoring
	pretty_name: HuSST
	---

	# Dataset Card for HuSST

	## Table of Contents

	- [Table of Contents](#table-of-contents)

	- [Dataset Description](#dataset-description)

	- [Dataset Summary](#dataset-summary)

	- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)

	- [Language](#language)

	- [Dataset Structure](#dataset-structure)

	- [Data Instances](#data-instances)

	- [Data Fields](#data-fields)

	- [Data Splits](#data-splits)

	- [Dataset Creation](#dataset-creation)

	- [Curation Rationale](#curation-rationale)

	- [Source Data](#source-data)

	- [Annotations](#annotations)

	- [Personal and Sensitive Information](#personal-and-sensitive-information)

	- [Considerations for Using the Data](#considerations-for-using-the-data)

	- [Social Impact of Dataset](#social-impact-of-dataset)

	- [Discussion of Biases](#discussion-of-biases)

	- [Other Known Limitations](#other-known-limitations)

	- [Additional Information](#additional-information)

	- [Dataset Curators](#dataset-curators)

	- [Licensing Information](#licensing-information)

	- [Citation Information](#citation-information)

	- [Contributions](#contributions)

	## Dataset Description
	- Homepage:
	- Repository:
	[HuSST dataset](https://github.com/nytud/HuSST)
	- Paper:
	- Leaderboard:
	- Point of Contact:
	[lnnoemi](mailto:ligeti-nagy.noemi@nytud.hu)

	### Dataset Summary

	This is the dataset card for the Hungarian version of the Stanford Sentiment Treebank. This dataset which is also part of the Hungarian Language Understanding Evaluation Benchmark Kit [HuLU](hulu.nlp.nytud.hu). The corpus was created by translating and re-annotating the original SST (Roemmele et al., 2011).

	### Supported Tasks and Leaderboards

	'sentiment classification'


	'sentiment scoring'

	### Language

	The BCP-47 code for Hungarian, the only represented language in this dataset, is hu-HU.

	## Dataset Structure

	### Data Instances

	For each instance, there is an id, a sentence and a sentiment label.

	An example:

	```
	{
	"Sent_id": "dev_0",
	"Sent": "Nos, a Jason elment Manhattanbe és a Pokolba kapcsán, azt hiszem, az elkerülhetetlen folytatások ötletlistájáról kihúzhatunk egy űrállomást 2455-ben (hé, ne lődd le a poént).",
	"Label": "neutral"
	}

	```

	### Data Fields

	- Sent_id: unique id of the instances;

	- Sent: the sentence, translation of an instance of the SST dataset;

	- Label: "negative", "neutral", or "positive".

	### Data Splits

	HuSST has 3 splits: train, validation and test.

	\| Dataset split \| Number of instances in the split \|
	\|---------------\|----------------------------------\|
	\| train \| 9344 \|
	\| validation \| 1168 \|
	\| test \| 1168 \|

	The test data is distributed without the labels. To evaluate your model, please [contact us](mailto:ligeti-nagy.noemi@nytud.hu), or check [HuLU's website](hulu.nlp.nytud.hu) for an automatic evaluation (this feature is under construction at the moment).

	## Dataset Creation

	### Source Data

	#### Initial Data Collection and Normalization

	The data is a translation of the content of the SST dataset (only the whole sentences were used). Each sentence was translated by a human translator. Each translation was manually checked and further refined by another annotator.

	### Annotations

	#### Annotation process

	The translated sentences were annotated by three human annotators with one of the following labels: negative, neutral and positive. Each sentence was then curated by a fourth annotator (the 'curator'). The final label is the decision of the curator based on the three labels of the annotators.

	#### Who are the annotators?

	The translators were native Hungarian speakers with English proficiency. The annotators were university students with some linguistic background.

	## Additional Information

	### Licensing Information


	### Citation Information

	If you use this resource or any part of its documentation, please refer to:

	Ligeti-Nagy, N., Ferenczi, G., Héja, E., Jelencsik-Mátyus, K., Laki, L. J., Vadász, N., Yang, Z. Gy. and Vadász, T. (2022) HuLU: magyar nyelvű benchmark adatbázis
	kiépítése a neurális nyelvmodellek kiértékelése céljából [HuLU: Hungarian benchmark dataset to evaluate neural language models]. XVIII. Magyar Számítógépes Nyelvészeti Konferencia. pp. 431–446.

	```

	@inproceedings{ligetinagy2022hulu,
	title={HuLU: magyar nyelvű benchmark adatbázis kiépítése a neurális nyelvmodellek kiértékelése céljából},
	author={Ligeti-Nagy, N. and Ferenczi, G. and Héja, E. and Jelencsik-Mátyus, K. and Laki, L. J. and Vadász, N. and Yang, Z. Gy. and Vadász, T.},
	booktitle={XVIII. Magyar Számítógépes Nyelvészeti Konferencia},
	year={2022},
	pages = {431--446}
	}

	```

	and to:

	Socher et al. (2013), Recursive Deep Models for Semantic Compositionality Over a Sentiment Treebank. In: Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing. 1631--1642.

	```

	@inproceedings{socher-etal-2013-recursive,
	title = "Recursive Deep Models for Semantic Compositionality Over a Sentiment Treebank",
	author = "Socher, Richard and
	Perelygin, Alex and
	Wu, Jean and
	Chuang, Jason and
	Manning, Christopher D. and
	Ng, Andrew and
	Potts, Christopher",
	booktitle = "Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing",
	month = oct,
	year = "2013",
	address = "Seattle, Washington, USA",
	publisher = "Association for Computational Linguistics",
	url = "https://www.aclweb.org/anthology/D13-1170",
	pages = "1631--1642",
	}

	```

	### Contributions

	Thanks to [lnnoemi](https://github.com/lnnoemi) for adding this dataset.