Meta-learning For Vision-and-language Cross-lingual Transfer
Current pre-trained vison-language models (PVLMs) achieve excellent performance on a range of multi-modal datasets. Recent work has aimed at building multilingual models, and a range of novel multilingual multi-modal datasets have been proposed. Current PVLMs typically perform poorly on these datasets when used for multi-modal zero-shot or few-shot cross-lingual transfer, especially for low-resource languages. To alleviate this problem, we propose a novel meta-learning fine-tuning framework. Our framework makes current PVLMs rapidly adaptive to new languages in vision-language scenarios by designing MAML in a cross-lingual multi-modal manner. Experiments show that our method boosts the performance of current state-of-the-art PVLMs in both zero-shot and few-shot cross-lingual transfer on a range of vision-language understanding tasks and datasets (XVNLI, xGQA, MaRVL, xFlicker&Co)
Code (0)
등록된 구현이 없습니다.
Tasks
Cross-Lingual TransferMeta-LearningMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Meta-X_{NLG}: A Meta-Learning Approach Based on Language Clustering for Zero-Shot Cross-Lingual Transfer and Generation
Recently, the NLP community has witnessed a rapid advancement in multilingual and cross-lingual transfer research where the supervision is transferred from high-resource languages (HRLs) to low-resource languages (LRLs).…
Abstractive Text SummarizationCross-Lingual TransferMeta-LearningQuestion Generation+3Meta-X$_{NLG}$: A Meta-Learning Approach Based on Language Clustering for Zero-Shot Cross-Lingual Transfer and Generation
Recently, the NLP community has witnessed a rapid advancement in multilingual and cross-lingual transfer research where the supervision is transferred from high-resource languages (HRLs) to low-resource languages (LRLs).…
Abstractive Text SummarizationCross-Lingual TransferMeta-LearningQuestion Generation+3Cross-Lingual Language Model Meta-Pretraining
The success of pretrained cross-lingual language models relies on two essential abilities, i.e., generalization ability for learning downstream tasks in a source language, and cross-lingual transferability for transferri…
Cross-Lingual TransferLanguage ModelingLanguage ModellingmodelMetaXL: Meta Representation Transformation for Low-resource Cross-lingual Learning
The combination of multilingual pre-trained representations and cross-lingual transfer learning is one of the most effective methods for building functional NLP systems for low-resource languages. However, for extremely …
Cross-Lingual TransferMeta-Learningnamed-entity-recognitionNamed Entity Recognition+3X-METRA-ADA: Cross-lingual Meta-Transfer Learning Adaptation to Natural Language Understanding and Question Answering
Multilingual models, such as M-BERT and XLM-R, have gained increasing popularity, due to their zero-shot cross-lingual transfer learning capabilities. However, their generalization ability is still inconsistent for typol…
Cross-Lingual TransferMeta-LearningNatural Language UnderstandingQuestion Answering+3