MAD-G: Multilingual Adapter Generation for Efficient Cross-Lingual Transfer (2021.findings-emnlp)
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Alan Ansell, Edoardo Maria Ponti, Jonas Pfeiffer, Sebastian Ruder, Goran Glavaš, Ivan Vulić, Anna Korhonen
| Challenge: | Massively multilingual transformers (MMTs) have benefited from additional training of language-specific adapters, but this approach is not viable for the vast majority of languages due to limitations in their corpus size or compute budgets. |
| Approach: | They propose a multilingual ADapter generation approach which contextually generates language adapters from language representations based on typological features. |
| Outcome: | The proposed method improves cross-lingual transfer performance on part-of-speech tagging, dependency parsing, and named entity recognition tasks while remaining cost-effective. |
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