@inproceedings{mitra-etal-2020-iitkgp,
title = "{IIT}kgp at {F}in{C}ausal 2020, Shared Task 1: Causality Detection using Sentence Embeddings in Financial Reports",
author = "Mitra, Arka and
Srivastava, Harshvardhan and
Tiwari, Yugam",
editor = "El-Haj, Dr Mahmoud and
Athanasakou, Dr Vasiliki and
Ferradans, Dr Sira and
Salzedo, Dr Catherine and
Elhag, Dr Ans and
Bouamor, Dr Houda and
Litvak, Dr Marina and
Rayson, Dr Paul and
Giannakopoulos, Dr George and
Pittaras, Nikiforos",
booktitle = "Proceedings of the 1st Joint Workshop on Financial Narrative Processing and MultiLing Financial Summarisation",
month = dec,
year = "2020",
address = "Barcelona, Spain (Online)",
publisher = "COLING",
url = "https://aclanthology.org/2020.fnp-1.16",
pages = "95--99",
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.",
}
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%0 Conference Proceedings
%T IITkgp at FinCausal 2020, Shared Task 1: Causality Detection using Sentence Embeddings in Financial Reports
%A Mitra, Arka
%A Srivastava, Harshvardhan
%A Tiwari, Yugam
%Y El-Haj, Dr Mahmoud
%Y Athanasakou, Dr Vasiliki
%Y Ferradans, Dr Sira
%Y Salzedo, Dr Catherine
%Y Elhag, Dr Ans
%Y Bouamor, Dr Houda
%Y Litvak, Dr Marina
%Y Rayson, Dr Paul
%Y Giannakopoulos, Dr George
%Y Pittaras, Nikiforos
%S Proceedings of the 1st Joint Workshop on Financial Narrative Processing and MultiLing Financial Summarisation
%D 2020
%8 December
%I COLING
%C Barcelona, Spain (Online)
%F mitra-etal-2020-iitkgp
%X 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.
%U https://aclanthology.org/2020.fnp-1.16
%P 95-99
Markdown (Informal)
[IITkgp at FinCausal 2020, Shared Task 1: Causality Detection using Sentence Embeddings in Financial Reports](https://aclanthology.org/2020.fnp-1.16) (Mitra et al., FNP 2020)
ACL