Abstract
This paper describes work performed for the FinCasual 2022 Shared Task “Financial Document Causality Detection” (FinCausal 2022). As the name implies, the task involves extraction of casual and consequential elements from financial text. Our approach focuses employing Nested NER using the Text-to-Text Transformer (T5) generative transformer models while applying different combinations of datasets and tagging methods. Our system reports accuracy of 79% in Exact Match comparison and F-measure score of 92% token level measurement.- Anthology ID:
- 2022.fnp-1.24
- Volume:
- Proceedings of the 4th Financial Narrative Processing Workshop @LREC2022
- Month:
- June
- Year:
- 2022
- Address:
- Marseille, France
- Editors:
- Mahmoud El-Haj, Paul Rayson, Nadhem Zmandar
- Venue:
- FNP
- SIG:
- Publisher:
- European Language Resources Association
- Note:
- Pages:
- 135–138
- Language:
- URL:
- https://aclanthology.org/2022.fnp-1.24
- DOI:
- Cite (ACL):
- Jooyeon Lee, Luan Huy Pham, and Özlem Uzuner. 2022. MNLP at FinCausal2022: Nested NER with a Generative Model. In Proceedings of the 4th Financial Narrative Processing Workshop @LREC2022, pages 135–138, Marseille, France. European Language Resources Association.
- Cite (Informal):
- MNLP at FinCausal2022: Nested NER with a Generative Model (Lee et al., FNP 2022)
- PDF:
- https://preview.aclanthology.org/naacl24-info/2022.fnp-1.24.pdf