mlsanityData to 14 Oct 2026

FA Team at the NTCIR-17 UFO Task

arXiv 2310.20322 ↗First posted 31 Oct 2023cs.CL cs.IRcited by 0

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Abstract (arXiv)

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.

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References · 3

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
  2. 2019TabFact: A Large-scale Dataset for Table-based Fact Verificationcited by 428
  3. 2019Portuguese Named Entity Recognition using BERT-CRFcited by 73

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