JDD @ F in C ausal 2020, Task 2: Financial Document Causality Detection (2020)

Abstract: This paper describes the approach we built for the Financial Document Causality Detection Shared Task (FinCausal-2020) Task 2: Cause and Effect Detection. Our approach is based on a multi-class classifier using BiLSTM with Graph Convolutional Neural Network (GCN) trained by minimizing the binary cross entropy loss. In our approach, we have not used any extra data source apart from combining the trial and practice dataset. We achieve weighted F1 score to 75.61 percent and are ranked at 7-th place.

## introduction causal detection is one of the major concrete tasks on information extraction (ie) in nlp research. having a causality in text can be defined as an identification of a pair of sub-strings which explain the cause-effect relationship. how a financial news (political / economical / financial events etc.) links to the other is extremely useful for analysts, investors and risk managers in bank to forecast what will happen in the (near) future in the domestic / global economy and the financial market. fincausal-2020 shared task is associated with a joint workshop on financial narrative processing and multiling financial summarisation @xcite . it consists of the two sub-tasks: ## task and dataset we first describe the task setting and dataset organization. the objective of the task2 is to extract a cause-effect pair from each sample document in the following format. id text cause effect 0026.00062 it's risen 106,500% since it came on the scene a split-adjusted @xmath01.50 a share. it's risen 106,500% the below table shows statistics of all the dataset provided by organizer for each in csv file. "evaluation" dataset is aimed for the bind test; therefore, it has exactly the same format but both the "cause" and "effect" columns are left empty. please note that each document can consist of multiple sentences, but limited to less than or equal to five sentences. also, multiple (different) cause-effect pairs could be found in the same document. in that case, they are distinguished in their ids by adding an extra separator (ex. 0026.00070.1, 0026.00070.2, 0026.00070.3, and so on). the prediction results are evaluated using the following weighted f1 score (= f 1 weighted ) and the number of exact matches (= em ): ## experimental evaluation this section evaluates our approach using the shared task 2 dataset. foot_2 ## conclusion in this paper, we propose a method for extracting cause and effect parts from texts. we first consider this task to be the sequence labeling task and then, applied the method for de to this task in a reasonable manner. our approach was ranked at 7-th. in future, our approach can be improved by utilizing the pre-trained language models (e.g., bert) or transfer learning approaches.