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Learn to Cross-lingual Transfer with Meta Graph Learning Across Heterogeneous Languages
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
The recent emergence of multilingual pretraining language model (mPLM) has enabled breakthroughs on various downstream crosslingual transfer (CLT) tasks. However, mPLMbased methods usually involve two problems: (1) simply fine-tuning may not adapt generalpurpose multilingual representations to be task-aware on low-resource languages; (2) ignore how cross-lingual adaptation happens for downstream tasks. To address the issues, we propose a meta graph learning (MGL) method. Unlike prior works thatdoi:10.18653/v1/2020.emnlp-main.179 fatcat:at5hl33n6zdr3ihuh4fw7gueje