Datasets:
metadata
dataset_info:
- config_name: all_languages_highlevel
features:
- name: text
dtype: string
- name: label
dtype: string
- name: lang
dtype: string
- name: id
dtype: string
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- name: validation
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num_examples: 1608
- name: test
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num_examples: 2010
download_size: 601522
dataset_size: 1112475
- config_name: all_languages_lowlevel
features:
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- name: validation
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- name: test
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num_examples: 2010
download_size: 614714
dataset_size: 1295564
- config_name: high_resources_highlevel
features:
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- name: lang
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- name: id
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- name: validation
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download_size: 359821
dataset_size: 712242
- config_name: high_resources_lowlevel
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- name: validation
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- config_name: only_english_highlevel
features:
- name: text
dtype: string
- name: label
dtype: string
- name: lang
dtype: string
- name: id
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splits:
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- name: validation
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num_examples: 437
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dataset_size: 203220
- config_name: only_english_lowlevel
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- name: train
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num_examples: 1747
- name: validation
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num_examples: 437
download_size: 91808
dataset_size: 239819
configs:
- config_name: all_languages_highlevel
data_files:
- split: train
path: all_languages_highlevel/train-*
- split: validation
path: all_languages_highlevel/validation-*
- split: test
path: all_languages_highlevel/test-*
- config_name: all_languages_lowlevel
data_files:
- split: train
path: all_languages_lowlevel/train-*
- split: validation
path: all_languages_lowlevel/validation-*
- split: test
path: all_languages_lowlevel/test-*
- config_name: high_resources_highlevel
data_files:
- split: train
path: high_resources_highlevel/train-*
- split: validation
path: high_resources_highlevel/validation-*
- config_name: high_resources_lowlevel
data_files:
- split: train
path: high_resources_lowlevel/train-*
- split: validation
path: high_resources_lowlevel/validation-*
- config_name: only_english_highlevel
data_files:
- split: train
path: only_english_highlevel/train-*
- split: validation
path: only_english_highlevel/validation-*
- config_name: only_english_lowlevel
data_files:
- split: train
path: only_english_lowlevel/train-*
- split: validation
path: only_english_lowlevel/validation-*
task_categories:
- text-classification
language:
- en
- es
- pl
- hu
- el
- da
- tr
- ja
- sv
- fi
- 'no'
- ru
- it
- he
- is
tags:
- finance
size_categories:
- 1K<n<10K
MultiFin
MultiFin – a publicly available financial dataset consisting of real-world article headlines covering 15 languages across different writing systems and language families. The dataset consists of hierarchical label structure providing two classification tasks: multi-label and multi-class.
Dataset Description
The MULTIFIN dataset is a multilingual corpus, consisting of real-world article headlines covering 15 languages. The corpus is annotated using hierarchical label structure, providing two classification tasks: multi-class and multi-label classification.
- Curated by: Rasmus Jørgensen, Oliver Brandt, Mareike Hartmann, Xiang Dai, Christian Igel, and Desmond Elliott.
- Language(s) (NLP): English, Spanish, Polish, Hungarian, Greek, Danish, Turkish, Japanese, Swedish, Finnish, Norwegian, Russian, Italian, Hebrew, Icelandic.
- License: [More Information Needed]
Dataset Sources
- Repository: https://github.com/RasmusKaer/MultiFin
- Paper: https://aclanthology.org/2023.findings-eacl.66/
Dataset Structure
[More Information Needed]
Citation
BibTeX:
@inproceedings{jorgensen-etal-2023-multifin,
title = "{M}ulti{F}in: A Dataset for Multilingual Financial {NLP}",
author = "J{\o}rgensen, Rasmus and
Brandt, Oliver and
Hartmann, Mareike and
Dai, Xiang and
Igel, Christian and
Elliott, Desmond",
editor = "Vlachos, Andreas and
Augenstein, Isabelle",
booktitle = "Findings of the Association for Computational Linguistics: EACL 2023",
month = may,
year = "2023",
address = "Dubrovnik, Croatia",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.findings-eacl.66",
doi = "10.18653/v1/2023.findings-eacl.66",
pages = "894--909",
abstract = "Financial information is generated and distributed across the world, resulting in a vast amount of domain-specific multilingual data. Multilingual models adapted to the financial domain would ease deployment when an organization needs to work with multiple languages on a regular basis. For the development and evaluation of such models, there is a need for multilingual financial language processing datasets. We describe MultiFin {--} a publicly available financial dataset consisting of real-world article headlines covering 15 languages across different writing systems and language families. The dataset consists of hierarchical label structure providing two classification tasks: multi-label and multi-class. We develop our annotation schema based on a real-world application and annotate our dataset using both {`}label by native-speaker{'} and {`}translate-then-label{'} approaches. The evaluation of several popular multilingual models, e.g., mBERT, XLM-R, and mT5, show that although decent accuracy can be achieved in high-resource languages, there is substantial room for improvement in low-resource languages.",
}