Papers › Enhanced Meta-Learning for Cross-lingual Named Entity Recognition with Minimal Resources
Enhanced Meta-Learning for Cross-lingual Named Entity Recognition with Minimal Resources
Qianhui Wu, Zijia Lin, Guoxin Wang, Hui Chen, Börje F. Karlsson, Biqing Huang, Chin-Yew Lin
For languages with no annotated resources, transferring knowledge from rich-resource languages is an effective solution for named entity recognition (NER). While all existing methods directly transfer from source-learned model to a target language, in this paper, we propose to fine-tune the learned model with a few similar examples given a test case, which could benefit the prediction by leveraging the structural and semantic information conveyed in such similar examples. To this end, we present a meta-learning algorithm to find a good model parameter initialization that could fast adapt to the given test case and propose to construct multiple pseudo-NER tasks for meta-training by computing sentence similarities. To further improve the model's generalization ability across different languages, we introduce a masking scheme and augment the loss function with an additional maximum term during meta-training. We conduct extensive experiments on cross-lingual named entity recognition with minimal resources over five target languages. The results show that our approach significantly outperforms existing state-of-the-art methods across the board.
In Syntology Open this paper in Syntology's Atlas, the map of the papers in Syntology's graph and their citations.
Code
Repository list and official/mentioned flags are the archive's, frozen 2025-07-28. Reachability, where shown, is from one Syntology probe window (2026-09-16 to 2026-09-18); repositories not probed show nothing. GitHub stars are not tracked.
Code Syntology ran Syntology
Not run by Syntology. Nothing on this page verifies that the listed code works.
Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Cross-Lingual NER | CoNLL Dutch | Meta-Cross | F1 | 80.44 | #6 of 10 | Archive leaderboard | report |
| Cross-Lingual NER | CoNLL Dutch | Base Model | F1 | 79.57 | #8 of 10 | Archive leaderboard | report |
| Cross-Lingual NER | CoNLL German | Meta-Cross | F1 | 73.16 | #6 of 10 | Archive leaderboard | report |
| Cross-Lingual NER | CoNLL German | Base Model | F1 | 70.79 | #8 of 10 | Archive leaderboard | report |
| Cross-Lingual NER | CoNLL Spanish | Meta-Cross | F1 | 76.75 | #6 of 10 | Archive leaderboard | report |
| Cross-Lingual NER | CoNLL Spanish | Base Model | F1 | 74.59 | #9 of 10 | Archive leaderboard | report |
| Cross-Lingual NER | Europeana French | Meta-Cross | F1 | 55.3 | #1 of 2 | Archive leaderboard | report |
| Cross-Lingual NER | Europeana French | Base Model | F1 | 50.89 | #2 of 2 | Archive leaderboard | report |
| Cross-Lingual NER | MSRA | Meta-Cross | F1 | 77.89 | #1 of 2 | Archive leaderboard | report |
| Cross-Lingual NER | MSRA | Base Model | F1 | 76.42 | #2 of 2 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Methods
Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections