Datasets:
YAML tags: null
annotations_creators:
- found
language_creators:
- found
- expert-generated
languages:
- hu
licenses:
- cc-by-sa-4.0
multilinguality:
- monolingual
pretty_name: HuWSC
size_categories:
- unknown
source_datasets:
- extended|other
task_categories:
- structure-prediction
task_ids:
- coreference-resolution
Dataset Card for HuWNLI
Table of Contents
Dataset Description
- Homepage:
- Repository: HuWNLI dataset
- Paper:
- Leaderboard:
- Point of Contact: lnnoemi
Dataset Summary
This is the dataset card for the Hungarian translation of the Winograd schemas formatted as an inference task. A Winograd schema is a pair of sentences that differ in only one or two words and that contain an ambiguity that is resolved in opposite ways in the two sentences and requires the use of world knowledge and reasoning for its resolution (Levesque et al. 2012). This dataset is also part of the Hungarian Language Understanding Evaluation Benchmark Kit HuLU. The corpus was created by translating and manually curating the original English Winograd schemata. The NLI format was created by replacing the ambiguous pronoun with each possible referent (the method is described in GLUE's paper, Wang et al. 2019).
Languages
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 a schema, an id, two sentences and a label.
An example:
{"schema": "1",
"id": "0",
"sentence1": "A városi tanácstagok nem adtak engedélyt a tüntetőknek, mert kerülték az erőszakot.",
"sentence2": "A városi tanácstagok kerülték az erőszakot.",
"Label": "1"
}
Data Fields
schema: the number of the original schema this sentence pair was derived from;
id: unique id of the instances;
sentence1: the original sentence of the schema with one of the two alternate words;
sentence2: a manually formed question;
Label: "1" if sentence2 is entailed by sentence1, and "0" otherwise.
Data Splits
The data is distributed without any predefined splits.
Dataset Creation
Source Data
Initial Data Collection and Normalization
The data is a translation of the English Winograd schemas. Each schema was translated by a human translator. Each translation was manually checked and further refined by another annotator. Each schema was manually curated by a linguistic expert. The schemata were transformed into nli format by a linguistic expert.
Additional Information
Licensing Information
HuWSC is released under the BCreative Commons Attribution-ShareAlike 4.0 International License.
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 Váradi, T. (2022) HuLU: magyar nyelvű benchmark adatbázis kiépítése a neurális nyelvmodellek kiértékelése céljából [HuLU: Hungarian benchmark dataset to evaluate neural language models]. In: Berend, Gábor and Gosztolya, Gábor and Vincze, Veronika (eds), XVIII. Magyar Számítógépes Nyelvészeti Konferencia. JATEPress, Szeged. 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 Váradi, T.},
booktitle={XVIII. Magyar Számítógépes Nyelvészeti Konferencia},
year={2022},
editors = {Berend, Gábor and Gosztolya, Gábor and Vincze, Veronika},
address = {Szeged},
publisher = {JATEPress},
pages = {431–446}
}
and to:
Levesque, Hector, Davis, Ernest, Morgenstern, Leora (2012) he winograd schema challenge. In: Thirteenth International Conference on the Principles of Knowledge Representation and Reasoning.
@inproceedings{levesque2012winograd,
title={The Winograd Schema Challenge},
author={Levesque, Hector and Davis, Ernest and Morgenstern, Leora},
booktitle={Thirteenth International Conference on the Principles of Knowledge Representation and Reasoning},
year={2012},
organization={Citeseer}
}
Contributions
Thanks to lnnoemi for adding this dataset.