annotations_creators:
- other
language:
- sv
language_creators:
- other
multilinguality:
- monolingual
pretty_name: >-
A standardized suite for evaluation and analysis of Swedish natural language
understanding systems.
size_categories:
- unknown
source_datasets: []
task_categories:
- multiple-choice
- text-classification
- question-answering
- sentence-similarity
- token-classification
- summarization
task_ids:
- sentiment-analysis
- acceptability-classification
- closed-domain-qa
- word-sense-disambiguation
- coreference-resolution
Dataset Card for Superlim-2
Table of Contents
- Table of Contents
- Dataset Description
- Dataset Structure
- Dataset Creation
- Considerations for Using the Data
- Additional Information
Dataset Description
- Homepage: The official homepage of Språkbanken
- Repository:
- Paper:SwedishGLUE – Towards a Swedish Test Set for Evaluating Natural Language Understanding Models
- Leaderboard: https://lab.kb.se/leaderboard/
- Point of Contact:[email protected]
Dataset Summary
SuperLim 2.0 is a continuation of SuperLim 1.0, which aims for a standardized suite for evaluation and analysis of Swedish natural language understanding systems. The projects is inspired by the GLUE/SuperGLUE projects from which the name is derived: "lim" is the Swedish translation of "glue".
Since Superlim 2.0 is a collection of datasets, we refer for information about dataset structure, creation, social impact etc. to the specific data cards or documentation sheets in the official GitHub repository: https://github.com/spraakbanken/SuperLim-2/
Supported Tasks and Leaderboards
See our leaderboard: https://lab.kb.se/leaderboard/
Languages
Swedish
Dataset Structure
Data Instances
See individual datasets: https://github.com/spraakbanken/SuperLim-2/
Data Fields
See individual datasets: https://github.com/spraakbanken/SuperLim-2/
Data Splits
Most datasets have a train, dev and test split. However, there are a few (supersim, sweanalogy and swesat-synonyms) who only have a train and test split. The diagnostic tasks swediagnostics and swewinogender only have a test split, but they could be evaluated on models trained on swenli since they are also NLI-based.
Dataset Creation
Curation Rationale
See individual datasets: https://github.com/spraakbanken/SuperLim-2/
Source Data
Initial Data Collection and Normalization
See individual datasets: https://github.com/spraakbanken/SuperLim-2/
Who are the source language producers?
See individual datasets: https://github.com/spraakbanken/SuperLim-2/
Annotations
Annotation process
See individual datasets: https://github.com/spraakbanken/SuperLim-2/
Who are the annotators?
See individual datasets: https://github.com/spraakbanken/SuperLim-2/
Personal and Sensitive Information
See individual datasets: https://github.com/spraakbanken/SuperLim-2/
Considerations for Using the Data
Social Impact of Dataset
See individual datasets: https://github.com/spraakbanken/SuperLim-2/
Discussion of Biases
See individual datasets: https://github.com/spraakbanken/SuperLim-2/
Other Known Limitations
See individual datasets: https://github.com/spraakbanken/SuperLim-2/
Dataset Curators
See individual datasets: https://github.com/spraakbanken/SuperLim-2/
Licensing Information
All datasets constituting Superlim are available under Creative Commons licenses (CC BY 4.0, 8144 CC BY-SA 4.0, respectively).
Citation Information
To cite as a whole, use the standard reference. If you use or reference individual resources, cite the references specific for these resources:
Standard reference:
Superlim: A Swedish Language Understanding Evaluation Benchmark (Berdicevskis et al., EMNLP 2023)
@inproceedings{berdicevskis-etal-2023-superlim,
title = "Superlim: A {S}wedish Language Understanding Evaluation Benchmark",
author = {Berdicevskis, Aleksandrs and
Bouma, Gerlof and
Kurtz, Robin and
Morger, Felix and
{\"O}hman, Joey and
Adesam, Yvonne and
Borin, Lars and
Dann{\'e}lls, Dana and
Forsberg, Markus and
Isbister, Tim and
Lindahl, Anna and
Malmsten, Martin and
Rekathati, Faton and
Sahlgren, Magnus and
Volodina, Elena and
B{\"o}rjeson, Love and
Hengchen, Simon and
Tahmasebi, Nina},
editor = "Bouamor, Houda and
Pino, Juan and
Bali, Kalika",
booktitle = "Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing",
month = dec,
year = "2023",
address = "Singapore",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.emnlp-main.506",
doi = "10.18653/v1/2023.emnlp-main.506",
pages = "8137--8153",
abstract = "We present Superlim, a multi-task NLP benchmark and analysis platform for evaluating Swedish language models, a counterpart to the English-language (Super)GLUE suite. We describe the dataset, the tasks, the leaderboard and report the baseline results yielded by a reference implementation. The tested models do not approach ceiling performance on any of the tasks, which suggests that Superlim is truly difficult, a desirable quality for a benchmark. We address methodological challenges, such as mitigating the Anglocentric bias when creating datasets for a less-resourced language; choosing the most appropriate measures; documenting the datasets and making the leaderboard convenient and transparent. We also highlight other potential usages of the dataset, such as, for instance, the evaluation of cross-lingual transfer learning.",
}
Thanks to Felix Morger for adding this dataset.