Order of Intermediate Tasks in Sequential Multi-Task Learning for Zero-Shot Cross-Lingual Transfer on XTREME-R

Abstract

Abstract: Multilingual models jointly pretrained on multiple languages have achieved remarkable performance on various multilingual downstream tasks. Moreover, models finetuned on a single monolingual downstream task have shown to generalize to unseen languages. In this paper, we first show that it is crucial for those tasks to align gradients between them in order to maximize knowledge transfer while minimizing negative transfer. Despite its importance, the existing methods for gradient alignment either have a completely different purpose, ignore inter-task alignment, or aim to solve continual learning

Research Question

Does the order of intermediate tasks (sequential multi-task learning) affect zero-shot cross-lingual transfer performance on XTREME-R compared to single-task transfer?

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