|
--- |
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dataset_info: |
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- config_name: all_languages_highlevel |
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features: |
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- name: text |
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dtype: string |
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- name: label |
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dtype: string |
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- name: lang |
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dtype: string |
|
- name: id |
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|
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num_examples: 1608 |
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- name: test |
|
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num_examples: 2010 |
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download_size: 601522 |
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dataset_size: 1112475 |
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features: |
|
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|
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sequence: string |
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- name: lang |
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num_examples: 1608 |
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num_examples: 2010 |
|
download_size: 614714 |
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dataset_size: 1295564 |
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features: |
|
- name: text |
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download_size: 359821 |
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dataset_size: 712242 |
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features: |
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dtype: string |
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num_examples: 1339 |
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download_size: 368829 |
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dataset_size: 835073 |
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- config_name: only_english_highlevel |
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features: |
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- name: text |
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dtype: string |
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- name: label |
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dataset_size: 203220 |
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features: |
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- name: text |
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dtype: string |
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- name: labels |
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sequence: string |
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- name: lang |
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dtype: string |
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- name: id |
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num_examples: 1747 |
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num_examples: 437 |
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download_size: 91808 |
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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 |
|
|
|
<!-- Provide a quick summary of the dataset. --> |
|
|
|
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 |
|
|
|
<!-- Provide the basic links for the dataset. --> |
|
|
|
- **Repository:** https://github.com/RasmusKaer/MultiFin |
|
- **Paper:** https://aclanthology.org/2023.findings-eacl.66/ |
|
|
|
|
|
## Dataset Structure |
|
|
|
<!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. --> |
|
|
|
[More Information Needed] |
|
|
|
|
|
|
|
## Citation |
|
|
|
<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. --> |
|
|
|
**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.", |
|
} |
|
``` |
|
|
|
|
|
|
|
|
|
|
