MULTIFIN: A Dataset for Multilingual Financial NLP
Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review
Final published version, 395 KB, PDF document
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.
|Title of host publication||EACL 2023 - 17th Conference of the European Chapter of the Association for Computational Linguistics, Findings of EACL 2023|
|Publisher||Association for Computational Linguistics (ACL)|
|Publication status||Published - 2023|
|Event||17th Conference of the European Chapter of the Association for Computational Linguistics, EACL 2023 - Findings of EACL 2023 - Dubrovnik, Croatia|
Duration: 2 May 2023 → 6 May 2023
|Conference||17th Conference of the European Chapter of the Association for Computational Linguistics, EACL 2023 - Findings of EACL 2023|
|Periode||02/05/2023 → 06/05/2023|
|Sponsor||Adobe, Babelscape, Bloomberg Engineering, Duolingo, Liveperson|
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