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Automatic identification of cause-effect spans in financial documents isimportant for causality modelling and understanding reasons that lead tofinancial events. To exploit the observation that words are more connected toother words with the same cause-effect type in a dependency tree, we constructuseful graph embeddings by incorporating dependency relation features through agraph neural network. Our model builds on a baseline BERT token classifier withViterbi decoding, and outperforms this baseline in cross-validation and duringthe competition. In the official run of FinCausal 2021, we obtained Precision,Recall, and F1 scores of 95.56%, 95.56% and 95.57% that all ranked 1st place,and an Exact Match score of 86.05% which ranked 3rd place.

NUS-IDS at FinCausal 2021: Dependency Tree in Graph Neural Network for Better Cause-Effect Span Detection