论文标题
IIT_KGP在FinCausal 2020，共享任务1：使用句子嵌入财务报告中的因果关系检测
IIT_kgp at FinCausal 2020, Shared Task 1: Causality Detection using Sentence Embeddings in Financial Reports
论文作者
论文摘要
本文描述了团队提交给Fincausal 2020共享任务的工作。这项工作与识别句子中因果关系的第一个子任务有关。实验中使用的各种模型试图获得每个句子的潜在空间表示。对这些表示形式进行了线性回归，以分类该句子是否是因果关系。实验表明，BERT（大）表现最好，F1得分为0.958，以检测财务文本和报告中句子的因果关系。类不平衡处理了修改后的损失函数，以给评估提供更好的度量评分。
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.
