S^4-Tuning: A Simple Cross-lingual Sub-network Tuning Method

Abstract

The emergence of multilingual pre-trained language models makes it possible to adapt to target languages with only few labeled examples. However, vanilla fine-tuning tends to achieve degenerated and unstable results, owing to the Language Interference among different languages, and Parameter Overload under the few-sample transfer learning scenarios. To address two problems elegantly, we propose S^4-Tuning, a Simple Cross-lingual Sub-network Tuning method. S^4-Tuning first detects the most essential sub-network for each target language, and only updates it during fine-tuning. In this way, the language sub-networks lower the scale of trainable parameters, and hence better suit the low-resource scenarios. Meanwhile, the commonality and characteristics across languages are modeled by the overlapping and non-overlapping parts to ease the interference among languages. Simple but effective, S^4-Tuning gains consistent improvements over vanilla fine-tuning on three multi-lingual tasks involving 37 different languages in total (XNLI, PAWS-X, and Tatoeba).

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Reproductions