NUS-IDS at FinCausal 2021: Dependency Tree in Graph Neural Network for Better Cause-Effect Span Detection
This work addresses causality modeling in finance, offering an incremental improvement over existing methods for automated financial document analysis.
The paper tackled cause-effect span detection in financial documents by incorporating dependency tree features into a graph neural network, achieving top-ranked F1 scores of 95.57% and an Exact Match of 86.05% in the FinCausal 2021 competition.
Automatic identification of cause-effect spans in financial documents is important for causality modelling and understanding reasons that lead to financial events. To exploit the observation that words are more connected to other words with the same cause-effect type in a dependency tree, we construct useful graph embeddings by incorporating dependency relation features through a graph neural network. Our model builds on a baseline BERT token classifier with Viterbi decoding, and outperforms this baseline in cross-validation and during the 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.