Enhancing Cross-lingual Transfer by Manifold Mixup

2022/05/09 by Huiyun Yang, Huadong Chen, Yang, Huiyun +5 · 1 citation
Computer Science · #Natural Language Processing Techniques #Topic Modeling #Speech Recognition and Synthesis

paper · pdf · doi:10.48550/arxiv.2205.04182

Abstract

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.

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