论文标题
一种用于财务因果关系提取的生成方法
A Generative Approach for Financial Causality Extraction
论文作者
论文摘要
因果关系代表了财务文件（例如财务新闻文章）中事件之间的最大关系。每个财务因果关系都包含原因跨度和效果跨度。以前的作品提出了序列标签方法来解决此任务。但是，序列标记模型发现很难从文本段中提取多种因果关系和重叠因果关系。在本文中，我们使用Encoder-Decoder框架和指针网络探讨了一种因果关系提取的生成方法。我们使用金融领域的因果关系数据集，\ textit {Fincausal}进行实验，而我们提出的框架在此数据集上实现了非常有竞争力的性能。
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.
