Datasets:
pretty_name: Swiss-Judgment-Prediction
annotations_creators:
- found
- expert-generated
language_creators:
- found
language:
- de
- fr
- it
- en
license:
- cc-by-sa-4.0
multilinguality:
- multilingual
size_categories:
- 100K<n<1M
source_datasets:
- original
task_categories:
- text-classification
task_ids: []
tags:
- judgement-prediction
- explainability-judgment-prediction
dataset_info:
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dtype:
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Dataset Card for "SwissJudgmentPrediction"
Table of Contents
- Dataset Description
- Dataset Structure
- Dataset Creation
- Considerations for Using the Data
- Additional Information
Dataset Description
- Homepage: https://github.com/JoelNiklaus/SwissCourtRulingCorpus
- Repository: https://github.com/JoelNiklaus/SwissCourtRulingCorpus
- Paper: https://arxiv.org/abs/2110.00806
- Leaderboard: N/A
- Point of Contact: Joel Niklaus
Dataset Summary
Documents
Swiss-Judgment-Prediction is a multilingual, diachronic dataset of 85K Swiss Federal Supreme Court (FSCS) cases annotated with the respective binarized judgment outcome (approval/dismissal), posing a challenging text classification task. We also provide additional metadata, i.e., the publication year, the legal area and the canton of origin per case, to promote robustness and fairness studies on the critical area of legal NLP.
Supported Tasks and Leaderboards
SwissJudgmentPrediction can be used for the legal judgment prediction task.
OcclusionSwissJudgmentPrediction can be used for performing the occlusion in the legal judgment prediction task.
LowerCourtInsertionSwissJudgmentPrediction can be used for performing the LowerCourtInsertion in the legal judgment prediction task.
The dataset is part of the LEXTREME benchmark
Languages
Switzerland has four official languages with 3 languages (German, French and Italian) being represented in more than 1000 Swiss Federal Supreme court decisions. The decisions are written by the judges and clerks in the language of the proceedings.
Dataset Structure
In version 2 we added machine translated data using EasyNMT for all documents into German, French, Italian and English as an additional training set. With Occlusion-Swiss-Judgment-Prediction we extended part of the test set by adding sentence splitting with explainability labels. The Lower-Court-Insertion-Swiss-Judgment-Prediction extends part of the test set with lower court insertion. Note that both the test set for the Lower Court Insertion and Occlusion should be used in combination with the Swiss-Judgment-Prediction training and validation sets.
Data Instances
Multilingual use of the dataset
When the dataset is used in a multilingual setting selecting the the 'all_languages' flag:
from datasets import load_dataset
dataset = load_dataset('swiss_judgment_prediction', 'all_languages')
{
"id": 48757,
"year": 2015,
"facts": "Sachverhalt: A. X._ war bei der Krankenversicherung C._ taggeldversichert. Infolge einer Arbeitsunf\u00e4higkeit leistete ihm die C._ vom 30. Juni 2011 bis am 28. Juni 2013 Krankentaggelder, wobei die Leistungen bis am 30. September 2012 auf Grundlage einer Arbeitsunf\u00e4higkeit von 100% und danach basierend auf einer Arbeitsunf\u00e4higkeit von 55% erbracht wurden. Die Neueinsch\u00e4tzung der Arbeitsf\u00e4higkeit erfolgte anhand eines Gutachtens der D._ AG vom 27. August 2012, welches im Auftrag der C._ erstellt wurde. X._ machte daraufhin gegen\u00fcber der C._ geltend, er sei entgegen dem Gutachten auch nach dem 30. September 2012 zu 100% arbeitsunf\u00e4hig gewesen. Ferner verlangte er von der D._ AG zwecks externer \u00dcberpr\u00fcfung des Gutachtens die Herausgabe s\u00e4mtlicher diesbez\u00fcglicher Notizen, Auswertungen und Unterlagen. A._ (als Gesch\u00e4ftsf\u00fchrer der D._ AG) und B._ (als f\u00fcr das Gutachten medizinisch Verantwortliche) antworteten ihm, dass sie alle Unterlagen der C._ zugestellt h\u00e4tten und dass allf\u00e4llige Fragen zum Gutachten direkt der C._ zu stellen seien. X._ reichte am 2. Januar 2014 eine Strafanzeige gegen A._ und B._ ein. Er wirft diesen vor, ihn durch die Nichtherausgabe der Dokumente und durch Behinderung des IV-Verfahrens gen\u00f6tigt, Daten besch\u00e4digt bzw. vernichtet und ein falsches \u00e4rztliches Zeugnis ausgestellt zu haben. Zudem h\u00e4tten sie durch die Verz\u00f6gerung des IV-Verfahrens und insbesondere durch das falsche \u00e4rztliche Zeugnis sein Verm\u00f6gen arglistig gesch\u00e4digt. B. Die Staatsanwaltschaft des Kantons Bern, Region Oberland, nahm das Verfahren wegen N\u00f6tigung, Datenbesch\u00e4digung, falschem \u00e4rztlichem Zeugnis und arglistiger Verm\u00f6genssch\u00e4digung mit Verf\u00fcgung vom 10. November 2014 nicht an die Hand. Das Obergericht des Kantons Bern wies die von X._ dagegen erhobene Beschwerde am 27. April 2015 ab, soweit darauf einzutreten war. C. X._ beantragt mit Beschwerde in Strafsachen, der Beschluss vom 27. April 2015 sei aufzuheben und die Angelegenheit zur korrekten Ermittlung des Sachverhalts an die Staatsanwaltschaft zur\u00fcckzuweisen. Er stellt zudem den sinngem\u00e4ssen Antrag, das bundesgerichtliche Verfahren sei w\u00e4hrend der Dauer des konnexen Strafverfahrens gegen eine Teilgutachterin und des ebenfalls konnexen Zivil- oder Strafverfahrens gegen die C._ wegen Einsichtsverweigerung in das mutmasslich gef\u00e4lschte Originalgutachten zu sistieren. X._ ersucht um unentgeltliche Rechtspflege. ",
"labels": 0, # dismissal
"language": "de",
"region": "Espace Mittelland",
"canton": "be",
"legal area": "penal law"
}
Monolingual use of the dataset
When the dataset is used in a monolingual setting selecting the ISO language code for one of the 3 supported languages. For example:
from datasets import load_dataset
dataset = load_dataset('swiss_judgment_prediction', 'de')
{
"id": 48757,
"year": 2015,
"facts": "Sachverhalt: A. X._ war bei der Krankenversicherung C._ taggeldversichert. Infolge einer Arbeitsunf\u00e4higkeit leistete ihm die C._ vom 30. Juni 2011 bis am 28. Juni 2013 Krankentaggelder, wobei die Leistungen bis am 30. September 2012 auf Grundlage einer Arbeitsunf\u00e4higkeit von 100% und danach basierend auf einer Arbeitsunf\u00e4higkeit von 55% erbracht wurden. Die Neueinsch\u00e4tzung der Arbeitsf\u00e4higkeit erfolgte anhand eines Gutachtens der D._ AG vom 27. August 2012, welches im Auftrag der C._ erstellt wurde. X._ machte daraufhin gegen\u00fcber der C._ geltend, er sei entgegen dem Gutachten auch nach dem 30. September 2012 zu 100% arbeitsunf\u00e4hig gewesen. Ferner verlangte er von der D._ AG zwecks externer \u00dcberpr\u00fcfung des Gutachtens die Herausgabe s\u00e4mtlicher diesbez\u00fcglicher Notizen, Auswertungen und Unterlagen. A._ (als Gesch\u00e4ftsf\u00fchrer der D._ AG) und B._ (als f\u00fcr das Gutachten medizinisch Verantwortliche) antworteten ihm, dass sie alle Unterlagen der C._ zugestellt h\u00e4tten und dass allf\u00e4llige Fragen zum Gutachten direkt der C._ zu stellen seien. X._ reichte am 2. Januar 2014 eine Strafanzeige gegen A._ und B._ ein. Er wirft diesen vor, ihn durch die Nichtherausgabe der Dokumente und durch Behinderung des IV-Verfahrens gen\u00f6tigt, Daten besch\u00e4digt bzw. vernichtet und ein falsches \u00e4rztliches Zeugnis ausgestellt zu haben. Zudem h\u00e4tten sie durch die Verz\u00f6gerung des IV-Verfahrens und insbesondere durch das falsche \u00e4rztliche Zeugnis sein Verm\u00f6gen arglistig gesch\u00e4digt. B. Die Staatsanwaltschaft des Kantons Bern, Region Oberland, nahm das Verfahren wegen N\u00f6tigung, Datenbesch\u00e4digung, falschem \u00e4rztlichem Zeugnis und arglistiger Verm\u00f6genssch\u00e4digung mit Verf\u00fcgung vom 10. November 2014 nicht an die Hand. Das Obergericht des Kantons Bern wies die von X._ dagegen erhobene Beschwerde am 27. April 2015 ab, soweit darauf einzutreten war. C. X._ beantragt mit Beschwerde in Strafsachen, der Beschluss vom 27. April 2015 sei aufzuheben und die Angelegenheit zur korrekten Ermittlung des Sachverhalts an die Staatsanwaltschaft zur\u00fcckzuweisen. Er stellt zudem den sinngem\u00e4ssen Antrag, das bundesgerichtliche Verfahren sei w\u00e4hrend der Dauer des konnexen Strafverfahrens gegen eine Teilgutachterin und des ebenfalls konnexen Zivil- oder Strafverfahrens gegen die C._ wegen Einsichtsverweigerung in das mutmasslich gef\u00e4lschte Originalgutachten zu sistieren. X._ ersucht um unentgeltliche Rechtspflege. ",
"labels": 0, # dismissal
"language": "de",
"region": "Espace Mittelland",
"canton": "be",
"legal area": "penal law"
}
Data Fields
Multilingual use of the dataset
The following data fields are provided for documents (train, validation, test):
id: (int) a unique identifier of the for the document year: (int) the publication year text: (str) the facts of the case label: (class label) the judgment outcome: 0 (dismissal) or 1 (approval) language: (str) one of (de, fr, it) region: (str) the region of the lower court canton: (str) the canton of the lower court legal area: (str) the legal area of the case
Monolingual use of the dataset SwissJudgmentPrediction
The following data fields are provided for documents (train, validation, test):
id: (int) a unique identifier of the for the document year: (int) the publication year text: (str) the facts of the case label: (class label) the judgment outcome: 0 (dismissal) or 1 (approval) language: (str) one of (de, fr, it) region: (str) the region of the lower court canton: (str) the canton of the lower court legal area: (str) the legal area of the case
Monolingual use of the dataset OcclusionSwissJudgmentPrediction
The following data fields are provided for documents (occ_test):
id: (int) a unique identifier of the for the document year: (int) the publication year label: (str) the judgment outcome: dismissal or approval language: (str) one of (de, fr, it) region: (str) the region of the lower court canton: (str) the canton of the lower court legal area: (str) the legal area of the case explainability_label (str): the explainability label assigned to the occluded text: Supports judgment, Opposes judgment, Neutral, Baseline occluded_text (str): the occluded text text: (str) the facts of the case w/o the occluded text except for cases w/ explainability label "Baseline" (contain entire facts)
Note that Baseline cases are only contained in version 1 of the occlusion test set, since they do not change from experiment to experiment.
Monolingual use of the dataset LowerCourtInsertionSwissJudgmentPrediction
The following data fields are provided for documents (lci_test):
id: (int) a unique identifier of the for the document year: (int) the publication year label: (str) the judgment outcome: dismissal or approval language: (str) one of (de, fr, it) region: (str) the region of the lower court canton: (str) the canton of the lower court legal area: (str) the legal area of the case explainability_label: (str) the explainability label assigned to the occluded text: Lower court, Baseline text: (str) the facts of the case w/o the occluded text except for cases w/ explainability label "Baseline" (contain entire facts) lower_court: (str) the inserted lower_court (for Baseline there is no insertion)
Data Splits
| Language | Subset | Number of Documents (Training/Validation/Test) |
|---|---|---|
| German | de | 35'452 / 4'705 / 9'725 |
| French | fr | 21'179 / 3'095 / 6'820 |
| Italian | it | 3'072 / 408 / 812 |
| All | all | 59'709 / 8'208 / 17'357 |
| MT German | mt_de | 24'251 / 0 / 0 |
| MT French | mt_fr | 38'524 / 0 / 0 |
| MT Italian | mt_it | 56'631 / 0 / 0 |
| MT All | all+mt | 238'818 / 8'208 / 17'357 |
| LCI German | lci_de | 35'452 / 4'705 / 378 |
| LCI French | lci_fr | 21'179 / 3'095 / 414 |
| LCI Italian | lci_it | 3'072 / 408 / 335 |
| LCI All | lci+all | 59'709 / 8'208 / 1127 |
| OCC German | occ_de_1 | 35'452 / 4'705 / 427 |
| OCC German | occ_de_2 | 35'452 / 4'705 / 1366 |
| OCC German | occ_de_3 | 35'452 / 4'705 / 3567 |
| OCC German | occ_4 | 35'452 / 4'705 / 7235 |
| OCC French | occ_1 | 21'179 / 3'095 / 307 |
| OCC French | occ_2 | 21'179 / 3'095 / 854 |
| OCC French | occ_3 | 21'179 / 3'095 / 1926 |
| OCC French | occ_4 | 21'179 / 3'095 / 3279 |
| OCC Italian | occ_1 | 3'072 / 408 / 299 |
| OCC Italian | occ_2 | 3'072 / 408 / 919 |
| OCC Italian | occ_3 | 3'072 / 408 / 2493 |
| OCC Italian | occ_4 | 3'072 / 408 / 5733 |
| OCC All | occ+all | 59'709 / 8'208 / 28375 |
Dataset Creation
Curation Rationale
The dataset was curated by Niklaus et al. (2021) and Nina Baumgartner.
Source Data
Initial Data Collection and Normalization
The original data are available at the Swiss Federal Supreme Court (https://www.bger.ch) in unprocessed formats (HTML). The documents were downloaded from the Entscheidsuche portal (https://entscheidsuche.ch) in HTML.
Who are the source language producers?
Switzerland has four official languages with 3 languages (German, French and Italian) being represented in more than 1000 Swiss Federal Supreme court decisions. The decisions are written by the judges and clerks in the language of the proceedings.
Annotations
Annotation process
The decisions have been annotated with the binarized judgment outcome using parsers and regular expressions. In addition a subset of the test set (27 cases in German, 24 in French and 23 in Italian spanning over the years 2017 an 20200) was annotated by legal experts, splitting sentences/group of sentences and annotated with one of the following explainability label: Supports judgment, Opposes Judgment, Neutral and Lower Court. For the occlusion the test sets have each sentence/group of sentence once occluded, enabling an analysis of the changes in the model's performance. The lower court annotations were used the insert each lower court into each case once (instead of the original lower court). Allowing an analysis of the changes in the models performance for each inserted lower court, giving insight into a possible bias among them. The legal expert annotation were conducted from April 2020 to August 2020.
Who are the annotators?
Joel Niklaus and Adrian Jörg annotated the binarized judgment outcomes. Metadata is published by the Swiss Federal Supreme Court (https://www.bger.ch). The group of legal experts consists of Thomas Lüthi (lawyer), Lynn Grau (law student at master's level) and Angela Stefanelli (law student at master's level).
Personal and Sensitive Information
The dataset contains publicly available court decisions from the Swiss Federal Supreme Court. Personal or sensitive information has been anonymized by the court before publication according to the following guidelines: https://www.bger.ch/home/juridiction/anonymisierungsregeln.html.
Considerations for Using the Data
Social Impact of Dataset
[More Information Needed]
Discussion of Biases
[More Information Needed]
Other Known Limitations
[More Information Needed]
Additional Information
Dataset Curators
Niklaus et al. (2021)
Licensing Information
We release the data under CC-BY-4.0 which complies with the court licensing (https://www.bger.ch/files/live/sites/bger/files/pdf/de/urteilsveroeffentlichung_d.pdf)
© Swiss Federal Supreme Court, 2000-2020
The copyright for the editorial content of this website and the consolidated texts, which is owned by the Swiss Federal Supreme Court, is licensed under the Creative Commons Attribution 4.0 International licence. This means that you can re-use the content provided you acknowledge the source and indicate any changes you have made.
Source: https://www.bger.ch/files/live/sites/bger/files/pdf/de/urteilsveroeffentlichung_d.pdf
Citation Information
Joel Niklaus, Ilias Chalkidis, and Matthias Stürmer. Swiss-Judgment-Prediction: A Multilingual Legal Judgment Prediction Benchmark Proceedings of the 2021 Natural Legal Language Processing Workshop. Punta Cana, Dominican Republic. 2021
@InProceedings{niklaus-etal-2021-swiss,
author = {Niklaus, Joel
and Chalkidis, Ilias
and Stürmer, Matthias},
title = {Swiss-Judgment-Prediction: A Multilingual Legal Judgment Prediction Benchmark},
booktitle = {Proceedings of the 2021 Natural Legal Language Processing Workshop},
year = {2021},
location = {Punta Cana, Dominican Republic},
}
and the new citation
@misc{niklaus2022empirical,
title={An Empirical Study on Cross-X Transfer for Legal Judgment Prediction},
author={Joel Niklaus and Matthias Stürmer and Ilias Chalkidis},
year={2022},
eprint={2209.12325},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
@misc{baumgartner_nina_occlusion_2019,
title = {From Occlusion to Transparancy – An Occlusion-Based Explainability Approach for Legal Judgment Prediction in Switzerland},
shorttitle = {From Occlusion to Transparancy},
abstract = {Natural Language Processing ({NLP}) models have been used for more and more complex tasks such as Legal Judgment Prediction ({LJP}). A {LJP} model predicts the outcome of a legal case by utilizing its facts. This increasing deployment of Artificial Intelligence ({AI}) in high-stakes domains such as law and the involvement of sensitive data has increased the need for understanding such systems. We propose a multilingual occlusion-based explainability approach for {LJP} in Switzerland and conduct a study on the bias using Lower Court Insertion ({LCI}). We evaluate our results using different explainability metrics introduced in this thesis and by comparing them to high-quality Legal Expert Annotations using Inter Annotator Agreement. Our findings show that the model has a varying understanding of the semantic meaning and context of the facts section, and struggles to distinguish between legally relevant and irrelevant sentences. We also found that the insertion of a different lower court can have an effect on the prediction, but observed no distinct effects based on legal areas, cantons, or regions. However, we did identify a language disparity with Italian performing worse than the other languages due to representation inequality in the training data, which could lead to potential biases in the prediction in multilingual regions of Switzerland. Our results highlight the challenges and limitations of using {NLP} in the judicial field and the importance of addressing concerns about fairness, transparency, and potential bias in the development and use of {NLP} systems. The use of explainable artificial intelligence ({XAI}) techniques, such as occlusion and {LCI}, can help provide insight into the decision-making processes of {NLP} systems and identify areas for improvement. Finally, we identify areas for future research and development in this field in order to address the remaining limitations and challenges.},
author = {{Baumgartner, Nina}},
year = {2022},
langid = {english}
}
Contributions
Thanks to @joelniklaus and @ninabaumgartner for adding this dataset.