Abstract
The paper describes the work that the team submitted to FinCausal 2020 Shared Task. This work is associated with the first sub-task of identifying causality in sentences. The various models used in the experiments tried to obtain a latent space representation for each of the sentences. Linear regression was performed on these representations to classify whether the sentence is causal or not. The experiments have shown BERT (Large) performed the best, giving a F1 score of 0.958, in the task of detecting the causality of sentences in financial texts and reports. The class imbalance was dealt with a modified loss function to give a better metric score for the evaluation.- Anthology ID:
- 2020.fnp-1.16
- Volume:
- Proceedings of the 1st Joint Workshop on Financial Narrative Processing and MultiLing Financial Summarisation
- Month:
- December
- Year:
- 2020
- Address:
- Barcelona, Spain (Online)
- Editors:
- Dr Mahmoud El-Haj, Dr Vasiliki Athanasakou, Dr Sira Ferradans, Dr Catherine Salzedo, Dr Ans Elhag, Dr Houda Bouamor, Dr Marina Litvak, Dr Paul Rayson, Dr George Giannakopoulos, Nikiforos Pittaras
- Venue:
- FNP
- SIG:
- Publisher:
- COLING
- Note:
- Pages:
- 95–99
- Language:
- URL:
- https://aclanthology.org/2020.fnp-1.16
- DOI:
- Cite (ACL):
- Arka Mitra, Harshvardhan Srivastava, and Yugam Tiwari. 2020. IITkgp at FinCausal 2020, Shared Task 1: Causality Detection using Sentence Embeddings in Financial Reports. In Proceedings of the 1st Joint Workshop on Financial Narrative Processing and MultiLing Financial Summarisation, pages 95–99, Barcelona, Spain (Online). COLING.
- Cite (Informal):
- IITkgp at FinCausal 2020, Shared Task 1: Causality Detection using Sentence Embeddings in Financial Reports (Mitra et al., FNP 2020)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-4/2020.fnp-1.16.pdf