Enhancing Cross-lingual Transfer by Manifold Mixup

Autor:	Yang, Huiyun, Chen, Huadong, Zhou, Hao, Li, Lei
Rok vydání:	2022
Předmět:	Computer Science - Computation and Language Computer Science - Artificial Intelligence
Druh dokumentu:	Working Paper
Popis:	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. Comment: Accepted to ICLR2022
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2205.04182 Zobrazit plný text záznamu View this record from Arxiv