Extraction of Specific Arguments from Chinese Financial News with out-of-domain Samples

The object and evidence are two important parts of financial fraud detection (FFD). In traditional FFD tasks, the detected object generally refers to the enterprise involved in typical financial fraud events, and the evidence comes from corporate statements such as auditing report, etc. Thus, previous FFD methods based on financial statements are limited by the scope of detected objects and the availability of evidence. In this study, we design a financial event library to enlarge the detection scope and expand the source of evidence to financial news, and thus financial entities involved in high-risk events are extracted. The financial event library contains common negative events, each of which corresponds to a negative behaviour that may increase financial risk. Moreover, we propose a novel method to convert the event detection task into the Q&A mode task, which also contributes to the enlargement of the original hand-built dataset. Compared with existing methods on the enlargement of financial dataset, our approach does not require additional annotation. We apply our method into Chinese financial news corpus, and achieves good performance in the extraction task.