Datasets:
metadata
license: cc-by-4.0
language:
- sv
task_categories:
- text-classification
tags:
- causality
- swedish
- nlp
- ranking
- causality-detection
size_categories:
- n<1K
source_datasets:
- original
Swedish Causality Ranking Dataset
Causality ranking dataset for Swedish text, comparing sentence pairs on how well they match a causal prompt.
Dataset Description
This dataset contains pairs of Swedish sentences annotated on a 6-point scale for their causal relevance to a given prompt containing a cause or effect.
Fields
- prompt: Query containing a cause or effect (e.g., "Verkan: växthuseffekt")
- sentence_1_left_context: Context before sentence 1
- sentence_1_target: First target sentence
- sentence_1_right_context: Context after sentence 1
- sentence_2_left_context: Context before sentence 2
- sentence_2_target: Second target sentence
- sentence_2_right_context: Context after sentence 2
- annotation: Ranking score (1-6 scale)
Annotation Scale
The 6-point scale compares which sentence better expresses a causal relation matching the prompt:
- Lower scores: Sentence 1 is more relevant
- Higher scores: Sentence 2 is more relevant
- Middle scores: Both sentences are similarly relevant
Usage
from datasets import load_dataset
dataset = load_dataset("UppsalaNLP/swedish-causality-ranking")
# Access the data
data = dataset["train"]
# Example
print(data[0]["prompt"])
print(data[0]["sentence_1_target"])
print(data[0]["sentence_2_target"])
print(data[0]["annotation"])
Source
Text extracted from the SOU-corpus (Swedish Government Official Reports).
Citation
@inproceedings{durlich-etal-2022-cause,
title = "Cause and Effect in Governmental Reports: Two Data Sets for Causality Detection in Swedish",
author = "D{\"u}rlich, Luise and Reimann, Sebastian and Finnveden, Gustav and Nivre, Joakim and Stymne, Sara",
booktitle = "Proceedings of the First Workshop on Natural Language Processing for Political Sciences",
month = jun,
year = "2022",
address = "Marseilles, France"
}
License
This dataset is licensed under CC BY 4.0.