The FA team participated in the Table Data Extraction (TDE) and Text-to-Table Relationship Extraction (TTRE) tasks of the NTCIR-17 Understanding of Non-Financial Objects in Financial Reports (UFO). This paper reports our approach to solving the problems and discusses the official results. We successfully utilized various enhancement techniques based on the ELECTRA language model to extract valuable data from tables. Our efforts resulted in an impressive TDE accuracy rate of 93.43 %, positioning us in second place on the Leaderboard rankings. This outstanding achievement is a testament to our proposed approach’s effectiveness. In the TTRE task, we proposed the rule-based method to extract meaningful relationships between the text and tables task and confirmed the performance.

生成讲解博客的中文版本

生成AI智能校验的中文版本