Learning what to share between tasks has been a topic of high importance recently, as strategic sharing of knowledge has been shown to improve the performance of downstream tasks. The same applies to sharing between languages, and is especially important when considering the fact that most languages in the world suffer from being under-resourced. In this paper, we consider the setting of training models on multiple different languages at the same time, when little or no data is available for languages other than English. We show that this challenging setup can be approached using meta-learning, where, in addition to training a source language model, another model learns to select which training instances are the most beneficial. We experiment using standard supervised, zero-shot cross-lingual, as well as few-shot cross-lingual settings for different natural language understanding tasks (natural language inference, question answering). Our extensive experimental setup demonstrates the consistent effectiveness of meta-learning, on a total 16 languages. We improve upon state-of-the-art on zero-shot and few-shot NLI and QA tasks on the XNLI and X-WikiRe datasets, respectively. We further conduct a comprehensive analysis which indicates that correlation of typological features between languages can further explain when parameter sharing learned via meta learning is beneficial.

研究多语言应用中，如何应用元学习来提高模型性能，通过实验表明在标准的监督学习、零样本学习和少样本学习的跨语言自然语言理解任务中，元学习对于15种语言始终有效。

元学习实现零样本跨语言迁移