Research map / Label-efficient and robust learning / Recognition and long-tail
Cross-lingual Alignment vs Joint Training: A Comparative Study and A Simple Unified Framework
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Combine unsupervised joint initialization, vocabulary reallocation, and RCSLS alignment refinement | Joint_Align · MUSE BLI | P@1 avg: 72.4 -> 74.5 | Helped |
| Add vocabulary reallocation to unsupervised joint training, without alignment refinement (ablation) | Unsupervised Joint · MUSE BLI | P@1 avg: 29.3 -> 42.3 | Helped |
| Remove vocabulary reallocation while using RCSLS refinement (ablation) | Joint_Align + RCSLS · MUSE BLI | P@1 avg: 74.5 -> 29.0 | Hurt |
| Add a seed dictionary and randomly replace corpus words with their translations | Unsupervised Joint · MUSE BLI | P@1 avg: 29.3 -> 42.0 | Helped |
| Add alignment refinement to M-BERT extracted features | M-BERT Feature · CoNLL cross-lingual NER | F1 avg: 73.50 -> 75.11 | Helped |
Closest papers by meaning
- Cross-Lingual Transfer with Class-Weighted Language-Invariant Representations (ICLR 2022 · similarity 0.92)
- Normalization of Language Embeddings for Cross-Lingual Alignment (ICLR 2022 · similarity 0.91)
- XTREME: A Massively Multilingual Multi-task Benchmark for Evaluating Cross-lingual Generalisation (ICML 2020 · similarity 0.91)
- Multilingual Alignment of Contextual Word Representations (ICLR 2020 · similarity 0.90)
- Soft Token Alignment for Cross-Lingual Reasoning (NeurIPS 2026 · similarity 0.89)
- CL2CM: Improving Cross-Lingual Cross-Modal Retrieval via Cross-Lingual Knowledge Transfer (AAAI 2024 · similarity 0.88)
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