FinRead: A Transfer Learning Based Tool to Assess Readability of Definitions of Financial Terms

Sohom Ghosh, Shovon Sengupta, Sudip Naskar, Sunny Kumar Singh


Abstract
Simplified definitions of complex terms help learners to understand any content better. Comprehending readability is critical for the simplification of these contents. In most cases, the standard formula based readability measures do not hold good for measuring the complexity of definitions of financial terms. Furthermore, some of them works only for corpora of longer length which have at least 30 sentences. In this paper, we present a tool for evaluating readability of definitions of financial terms. It consists of a Light GBM based classification layer over sentence embeddings (Reimers et al., 2019) of FinBERT (Araci, 2019). It is trained on glossaries of several financial textbooks and definitions of various financial terms which are available on the web. The extensive evaluation shows that it outperforms the standard benchmarks by achieving a AU-ROC score of 0.993 on the validation set.
Anthology ID:
2021.icon-main.81
Volume:
Proceedings of the 18th International Conference on Natural Language Processing (ICON)
Month:
December
Year:
2021
Address:
National Institute of Technology Silchar, Silchar, India
Editors:
Sivaji Bandyopadhyay, Sobha Lalitha Devi, Pushpak Bhattacharyya
Venue:
ICON
SIG:
Publisher:
NLP Association of India (NLPAI)
Note:
Pages:
658–659
Language:
URL:
https://aclanthology.org/2021.icon-main.81
DOI:
Bibkey:
Cite (ACL):
Sohom Ghosh, Shovon Sengupta, Sudip Naskar, and Sunny Kumar Singh. 2021. FinRead: A Transfer Learning Based Tool to Assess Readability of Definitions of Financial Terms. In Proceedings of the 18th International Conference on Natural Language Processing (ICON), pages 658–659, National Institute of Technology Silchar, Silchar, India. NLP Association of India (NLPAI).
Cite (Informal):
FinRead: A Transfer Learning Based Tool to Assess Readability of Definitions of Financial Terms (Ghosh et al., ICON 2021)
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PDF:
https://aclanthology.org/2021.icon-main.81.pdf