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- W4298865718 abstract "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, FinCausal, for our experiments and our proposed framework achieves very competitive performance on this dataset." @default.
- W4298865718 created "2022-10-02" @default.
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- W4298865718 date "2022-04-25" @default.
- W4298865718 modified "2023-09-24" @default.
- W4298865718 title "A Generative Approach for Financial Causality Extraction" @default.
- W4298865718 cites W2997876626 @default.
- W4298865718 doi "https://doi.org/10.1145/3487553.3524633" @default.
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