On Negative Interference in Multilingual Models: Findings and A Meta-Learning Treatment

• URL: http://arxiv.org/abs/2010.03017v1
• Date: Tue, 6 Oct 2020 20:48:58 GMT
• Title: On Negative Interference in Multilingual Models: Findings and A Meta-Learning Treatment
• Authors: Zirui Wang, Zachary C. Lipton, Yulia Tsvetkov
• Abstract summary: We show that, contrary to previous belief, negative interference also impacts low-resource languages. We present a meta-learning algorithm that obtains better cross-lingual transferability and alleviates negative interference.
• Score: 59.995385574274785
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Modern multilingual models are trained on concatenated text from multiple languages in hopes of conferring benefits to each (positive transfer), with the most pronounced benefits accruing to low-resource languages. However, recent work has shown that this approach can degrade performance on high-resource languages, a phenomenon known as negative interference. In this paper, we present the first systematic study of negative interference. We show that, contrary to previous belief, negative interference also impacts low-resource languages. While parameters are maximally shared to learn language-universal structures, we demonstrate that language-specific parameters do exist in multilingual models and they are a potential cause of negative interference. Motivated by these observations, we also present a meta-learning algorithm that obtains better cross-lingual transferability and alleviates negative interference, by adding language-specific layers as meta-parameters and training them in a manner that explicitly improves shared layers' generalization on all languages. Overall, our results show that negative interference is more common than previously known, suggesting new directions for improving multilingual representations.

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