We present the FinCausal 2020 Shared Task on Causality Detection in Financial Documents and the associated FinCausal dataset, and discuss the participating systems and results. The task focuses on detecting if an object, an event or a chain of events is considered a cause for a prior event. This shared task focuses on determining causality associated with a quantified fact. An event is defined as the arising or emergence of a new object or context in regard to a previous situation. Therefore, the task will emphasise the detection of causality associated with transformation of financial objects embedded in quantified facts. A total number of 7 teams submitted system runs to the FinCausal task and contributed with a system description paper. FinCausal shared task is associated with the 4th Financial Narrative Processing Workshop (FNP 2022) (El-Haj et al., 2022) which is held at the The 13th Language Resources and Evaluation Conference (LREC 2022) in Marseille, France, on June 24, 2022.

We present the FinCausal 2020 Shared Task on Causality Detection in Financial Documents and the associated FinCausal dataset, and discuss the participating systems and results. Two sub-tasks are proposed: a binary classification task (Task 1) and a relation extraction task (Task 2). A total of 16 teams submitted runs across the two Tasks and 13 of them contributed with a system description paper. This workshop is associated to the Joint Workshop on Financial Narrative Processing and MultiLing Financial Summarisation (FNP-FNS 2020), held at The 28th International Conference on Computational Linguistics (COLING’2020), Barcelona, Spain on September 12, 2020.

In this paper, we explore strategies to detect and evaluate counterfactual sentences. We describe our system for SemEval-2020 Task 5: Modeling Causal Reasoning in Language: Detecting Counterfactuals. We use a BERT base model for the classification task and build a hybrid BERT Multi-Layer Perceptron system to handle the sequence identification task. Our experiments show that while introducing syntactic and semantic features does little in improving the system in the classification task, using these types of features as cascaded linear inputs to fine-tune the sequence-delimiting ability of the model ensures it outperforms other similar-purpose complex systems like BiLSTM-CRF in the second task. Our system achieves an F1 score of 85.00% in Task 1 and 83.90% in Task 2.