Finance-related news such as Bloomberg News, CNN Business and Forbes are
valuable sources of real data for market screening systems. In news, an expert
shares opinions beyond plain technical analyses that include context such as
political, sociological and cultural factors. In the same text, the expert
often discusses the performance of different assets. Some key statements are
mere descriptions of past events while others are predictions. Therefore,
understanding the temporality of the key statements in a text is essential to
separate context information from valuable predictions. We propose a novel
system to detect the temporality of finance-related news at discourse level
that combines Natural Language Processing and Machine Learning techniques, and
exploits sophisticated features such as syntactic and semantic dependencies.
More specifically, we seek to extract the dominant tenses of the main
statements, which may be either explicit or implicit. We have tested our system
on a labelled dataset of finance-related news annotated by researchers with
knowledge in the field. Experimental results reveal a high detection precision
compared to an alternative rule-based baseline approach. Ultimately, this
research contributes to the state-of-the-art of market screening by identifying
predictive knowledge for financial decision making.

通过自然语言处理和机器学习技术，我们提出一种能够在语篇层面检测金融相关新闻时态性的新系统，利用了句法和语义依赖等复杂特征，从而提取主要陈述的主导时态。实验结果表明，与一种基于规则的替代方法相比，该系统具有较高的检测精确度，最终有助于发掘金融决策的预测性知识。

通过结合自然语言处理和机器学习在金融新闻中检测篇章层面的时间性

Detection of Temporality at Discourse Level on Financial News by  Combining Natural Language Processing and Machine Learning

Non-parallel text style transfer is an important task in natural language
generation. However, previous studies concentrate on the token or sentence
level, such as sentence sentiment and formality transfer, but neglect long
style transfer at the discourse level. Long texts usually involve more
complicated author linguistic preferences such as discourse structures than
sentences. In this paper, we formulate the task of non-parallel story
author-style transfer, which requires transferring an input story into a
specified author style while maintaining source semantics. To tackle this
problem, we propose a generation model, named StoryTrans, which leverages
discourse representations to capture source content information and transfer
them to target styles with learnable style embeddings. We use an additional
training objective to disentangle stylistic features from the learned discourse
representation to prevent the model from degenerating to an auto-encoder.
Moreover, to enhance content preservation, we design a mask-and-fill framework
to explicitly fuse style-specific keywords of source texts into generation.
Furthermore, we constructed new datasets for this task in Chinese and English,
respectively. Extensive experiments show that our model outperforms strong
baselines in overall performance of style transfer and content preservation.

本文提出了一个名为 StoryTrans 的生成模型，该模型利用话语表示来捕捉源内容信息，并将其与可学习的样式嵌入一起传输到目标样式以解决非并行故事作者样式转换问题，并且使用一个附加的训练目标来解开样式特征，防止模型退化为一个自动编码器，并构建了新的汉英两种语言数据集，实验证明我们的模型在样式转换和内容保护的整体性能方面优于强基线。