Financial Numeric Extreme Labelling: A Dataset and Benchmarking for XBRL Tagging

• URL: http://arxiv.org/abs/2306.03723v1
• Date: Tue, 6 Jun 2023 14:41:30 GMT
• Title: Financial Numeric Extreme Labelling: A Dataset and Benchmarking for XBRL Tagging
• Authors: Soumya Sharma, Subhendu Khatuya, Manjunath Hegde, Afreen Shaikh. Koustuv Dasgupta, Pawan Goyal, Niloy Ganguly
• Abstract summary: The U.S. Securities and Exchange Commission (SEC) mandates all public companies to file periodic financial statements that should contain numerals with a particular label from a taxonomy. We formulate the task of formulate the task of a label to a particular numeral span in a sentence from an extremely large label set.
• Score: 23.01422165679548
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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