Financial Numeric Extreme Labelling: A dataset and benchmarking

Soumya Sharma, Subhendu Khatuya, Manjunath Hegde, Afreen Shaikh, Koustuv Dasgupta, Pawan Goyal, Niloy Ganguly

Abstract
The U.S. Securities and Exchange Commission (SEC) mandates all public companies to file periodic financial statements that should contain numerals annotated with a particular label from a taxonomy. In this paper, we formulate the task of automating the assignment of a label to a particular numeral span in a sentence from an extremely large label set. Towards this task, we release a dataset, Financial Numeric Extreme Labelling (FNXL), annotated with 2,794 labels. We benchmark the performance of the FNXL dataset by formulating the task as (a) a sequence labelling problem and (b) a pipeline with span extraction followed by Extreme Classification. Although the two approaches perform comparably, the pipeline solution provides a slight edge for the least frequent labels.
Anthology ID:
2023.findings-acl.219
Volume:
Findings of the Association for Computational Linguistics: ACL 2023
Month:
July
Year:
2023
Address:
Toronto, Canada
Editors:
Anna Rogers, Jordan Boyd-Graber, Naoaki Okazaki
Venue:
Findings
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
3550–3561
Language:
URL:
https://aclanthology.org/2023.findings-acl.219
DOI:
10.18653/v1/2023.findings-acl.219
Bibkey:
Cite (ACL):
Soumya Sharma, Subhendu Khatuya, Manjunath Hegde, Afreen Shaikh, Koustuv Dasgupta, Pawan Goyal, and Niloy Ganguly. 2023. Financial Numeric Extreme Labelling: A dataset and benchmarking. In Findings of the Association for Computational Linguistics: ACL 2023, pages 3550–3561, Toronto, Canada. Association for Computational Linguistics.
Cite (Informal):
Financial Numeric Extreme Labelling: A dataset and benchmarking (Sharma et al., Findings 2023)
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