A Generative Approach for Financial Causality Extraction
This addresses the challenge of causality extraction in financial texts, which is crucial for analyzing events in news and reports, but the approach appears incremental as it builds on existing generative methods for a specific domain task.
The paper tackled the problem of extracting multiple and overlapping causalities from financial documents, which previous sequence labeling models struggled with, by proposing a generative approach using an encoder-decoder framework and pointer networks, achieving very competitive performance on the FinCausal dataset.
Causality represents the foremost relation between events in financial documents such as financial news articles, financial reports. Each financial causality contains a cause span and an effect span. Previous works proposed sequence labeling approaches to solve this task. But sequence labeling models find it difficult to extract multiple causalities and overlapping causalities from the text segments. In this paper, we explore a generative approach for causality extraction using the encoder-decoder framework and pointer networks. We use a causality dataset from the financial domain, \textit{FinCausal}, for our experiments and our proposed framework achieves very competitive performance on this dataset.