Papers › Cross-Lingual Named Entity Recognition Using Parallel Corpus: A New Approach Using...
Cross-Lingual Named Entity Recognition Using Parallel Corpus: A New Approach Using XLM-RoBERTa Alignment
Bing Li, Yujie He, Wenjin Xu
We propose a novel approach for cross-lingual Named Entity Recognition (NER) zero-shot transfer using parallel corpora. We built an entity alignment model on top of XLM-RoBERTa to project the entities detected on the English part of the parallel data to the target language sentences, whose accuracy surpasses all previous unsupervised models. With the alignment model we can get pseudo-labeled NER data set in the target language to train task-specific model. Unlike using translation methods, this approach benefits from natural fluency and nuances in target-language original corpus. We also propose a modified loss function similar to focal loss but assigns weights in the opposite direction to further improve the model training on noisy pseudo-labeled data set. We evaluated this proposed approach over 4 target languages on benchmark data sets and got competitive F1 scores compared to most recent SOTA models. We also gave extra discussions about the impact of parallel corpus size and domain on the final transfer performance.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Cross-Lingual NER | CoNLL 2003 | XLM-RoBERTa-large | Dutch | 79.7 | #2 of 4 | Archive leaderboard | report |
| Cross-Lingual NER | CoNLL 2003 | XLM-RoBERTa-large | German | 76.9 | #2 of 4 | Archive leaderboard | report |
| Cross-Lingual NER | CoNLL 2003 | XLM-RoBERTa-large | Spanish | 78.9 | #2 of 4 | 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.
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