因果关系(物理学)
计算机科学
生成语法
指针(用户界面)
序列(生物学)
生成模型
人工智能
财务
经济
遗传学
量子力学
生物
物理
作者
Tapas Nayak,Soumya Sharma,Yash Butala,Koustuv Dasgupta,Pawan Goyal,Niloy Ganguly
出处
期刊:Companion Proceedings of the The Web Conference 2018
日期:2022-04-25
卷期号:: 576-578
被引量:6
标识
DOI:10.1145/3487553.3524633
摘要
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.
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