论文标题
通过歧管混合增强跨语性转移
Enhancing Cross-lingual Transfer by Manifold Mixup
论文作者
论文摘要
基于大规模的预训练多语言表示，最近的跨语性转移方法实现了令人印象深刻的转移性能。但是，目标语言的性能仍然远远落后于源语言。在本文中，我们的分析表明，这种性能差距与跨语性表示差异密切相关。为了实现更好的跨语性转移性能，我们提出了跨语义歧管混音（X-MIXUP）方法，该方法可适应地校准表示形式的差异并为目标语言提供妥协的表示形式。 Xtreme基准上的实验表明，与强基础相比，X-Mixup在多个文本理解任务上获得了1.8％的性能增长，并显着降低了跨语义表示的差异。
Based on large-scale pre-trained multilingual representations, recent cross-lingual transfer methods have achieved impressive transfer performances. However, the performance of target languages still lags far behind the source language. In this paper, our analyses indicate such a performance gap is strongly associated with the cross-lingual representation discrepancy. To achieve better cross-lingual transfer performance, we propose the cross-lingual manifold mixup (X-Mixup) method, which adaptively calibrates the representation discrepancy and gives a compromised representation for target languages. Experiments on the XTREME benchmark show X-Mixup achieves 1.8% performance gains on multiple text understanding tasks, compared with strong baselines, and significantly reduces the cross-lingual representation discrepancy.
