SPOCK at FinCausal 2022: Causal Information Extraction Using Span-Based and Sequence Tagging Models
Anik Saha, Jian Ni, Oktie Hassanzadeh, Alex Gittens, Kavitha Srinivas, Bulent Yener
Abstract
Causal information extraction is an important task in natural language processing, particularly in finance domain. In this work, we develop several information extraction models using pre-trained transformer-based language models for identifying cause and effect text spans from financial documents. We use FinCausal 2021 and 2022 data sets to train span-based and sequence tagging models. Our ensemble of sequence tagging models based on the RoBERTa-Large pre-trained language model achieves an F1 score of 94.70 with Exact Match score of 85.85 and obtains the 1st place in the FinCausal 2022 competition.- Anthology ID:
- 2022.fnp-1.17
- 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:
- 108–111
- Language:
- URL:
- https://aclanthology.org/2022.fnp-1.17
- DOI:
- Cite (ACL):
- Anik Saha, Jian Ni, Oktie Hassanzadeh, Alex Gittens, Kavitha Srinivas, and Bulent Yener. 2022. SPOCK at FinCausal 2022: Causal Information Extraction Using Span-Based and Sequence Tagging Models. In Proceedings of the 4th Financial Narrative Processing Workshop @LREC2022, pages 108–111, Marseille, France. European Language Resources Association.
- Cite (Informal):
- SPOCK at FinCausal 2022: Causal Information Extraction Using Span-Based and Sequence Tagging Models (Saha et al., FNP 2022)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-1/2022.fnp-1.17.pdf