  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.
