Cross-lingual Extended Named Entity Classification of Wikipedia Articles
FPT Technology Research Institute（FPT技术研究院）；FPT University（FPT大学）；Vietnam National University, Hanoi（河内国家大学）
英文摘要：
The FPT.AI team participated in the SHINRA2020-ML subtask of the NTCIR-15 SHINRA task. This paper describes our method to solving the problem and discusses the official results. Our method focuses on learning cross-lingual representations, both on the word level and document level for page classification. We propose a three-stage approach including multilingual model pre-training, monolingual model fine-tuning and cross-lingual voting. Our system is able to achieve the best scores for 25 out of 30 languages; and its accuracy gaps to the best performing systems of the other five languages are relatively small.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
