Cross Lingual Ner On Conll German

評価結果

このベンチマークにおける各モデルのパフォーマンス結果

		Paper Title
SMTS Multi sim	75.33	Single-/Multi-Source Cross-Lingual NER via Teacher-Student Learning on Unlabeled Data in Target Language
SMTS Multi avg	74.97	Single-/Multi-Source Cross-Lingual NER via Teacher-Student Learning on Unlabeled Data in Target Language
UniTrans	74.82	UniTrans: Unifying Model Transfer and Data Transfer for Cross-Lingual Named Entity Recognition with Unlabeled Data
XLM-R large	74.5	Model and Data Transfer for Cross-Lingual Sequence Labelling in Zero-Resource Settings
SMTS Single	73.22	Single-/Multi-Source Cross-Lingual NER via Teacher-Student Learning on Unlabeled Data in Target Language
Meta-Cross	73.16	Enhanced Meta-Learning for Cross-lingual Named Entity Recognition with Minimal Resources
Zero shot mBERT 3	72.44	Towards Lingua Franca Named Entity Recognition with BERT
Base Model	70.79	Enhanced Meta-Learning for Cross-lingual Named Entity Recognition with Minimal Resources
mBERT	69.56	Beto, Bentz, Becas: The Surprising Cross-Lingual Effectiveness of BERT
MAN-MoE+CharCNN+UMWE	56	Multi-Source Cross-Lingual Model Transfer: Learning What to Share

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