Tomoki Ito


JDD @ FinCausal 2020, Task 2: Financial Document Causality Detection
Toshiya Imoto | Tomoki Ito
Proceedings of the 1st Joint Workshop on Financial Narrative Processing and MultiLing Financial Summarisation

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.

Learning Company Embeddings from Annual Reports for Fine-grained Industry Characterization
Tomoki Ito | Jose Camacho Collados | Hiroki Sakaji | Steven Schockaert
Proceedings of the Second Workshop on Financial Technology and Natural Language Processing