FinKENet:一个新的金融知识增强网络,用于金融问题匹配
Yu Guo1, Ting Liang2, Zhongpu Chen1
1Financial Intelligence and Financial Engineering Key Laboratory of Sichuan Province, Fintech Innovation Center, Southwestern University of Finance and Economics, Chengdu 611130, China.
一种新的方法,金融知识增强网络 (FinKENet),通过更好地了解金融背景和关键词,改善了金融问题匹配. 这增强了金融领域的基于检索的对话系统.
科学领域:
- 自然语言处理自然语言处理.
- 信息检索 信息检索
- 金融技术 金融技术
背景情况:
- 现有的问题匹配方法缺乏针对金融文本的特定领域的调整.
- 金融问题匹配在建模上下文表示和金融关键短语方面面临挑战.
研究的目的:
- 提出一个新的金融知识增强网络 (FinKENet),以改善财务问题匹配.
- 解决专业金融部门的通用领域方法的局限性.
主要方法:
- 开发了一种多级编码器,用于提取句子级和金融短语级的特征.
- 引入了财务共同关注适配器,以整合句子和关键字功能.
- 设计了一个多级相似性解码器和一个交叉损失函数用于优化.
主要成果:
- 在金融问题匹配数据集上,FinKENet模型证明了其有效性.
- 在回忆得分方面实现了1.69%的绝对改善,从73.21%增加到74.90%.
结论:
- 通过注入特定领域的知识,FinKENet显著提高了财务问题匹配.
- 拟议的方法提供了更准确的财务句子和关键词的表现,改善对话系统的性能.
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