论文标题
使用有条件的差异自动编码器与知识蒸馏产生长期财务报告
Generating Long Financial Report using Conditional Variational Autoencoders with Knowledge Distillation
论文作者
论文摘要
自动从新闻中生成财务报告是一项艰巨的任务。显然，这项任务的困难在于缺乏足够的背景知识来有效产生长期财务报告。为了解决这个问题，本文提出了基于条件变化自动编码器（CVAE）方法，该方法将外部知识从新闻报道数据的语料库中提取。特别是，我们选择BI-GRU作为CVAE的编码器和解码器组件，并从输入新闻中学习潜在变量分布。从一组新闻报道数据集中学到了更高级别的潜在变量分布，分别为每个输入新闻作用，以向先前学习的潜在变量分布提供背景知识。然后，使用教师学生网络来提炼知识来完善解码器组件的外发。为了评估所提出方法的模型性能，在实验中选择了公共数据集中广泛的实验，并在公共数据集中进行了两个广泛采用的评估标准，即Bleu和Rouge。有希望的实验结果表明，所提出的方法优于其余方法。
Automatically generating financial report from a piece of news is quite a challenging task. Apparently, the difficulty of this task lies in the lack of sufficient background knowledge to effectively generate long financial report. To address this issue, this paper proposes the conditional variational autoencoders (CVAE) based approach which distills external knowledge from a corpus of news-report data. Particularly, we choose Bi-GRU as the encoder and decoder component of CVAE, and learn the latent variable distribution from input news. A higher level latent variable distribution is learnt from a corpus set of news-report data, respectively extr acted for each input news, to provide background knowledge to previously learnt latent variable distribution. Then, a teacher-student network is employed to distill knowledge to refine theoutput of the decoder component. To evaluate the model performance of the proposed approach, extensive experiments are preformed on a public dataset and two widely adopted evaluation criteria, i.e., BLEU and ROUGE, are chosen in the experiment. The promising experimental results demonstrate that the proposed approach is superior to the rest compared methods.
