Cross-lingual Lifelong Learning

The longstanding goal of multi-lingual learning has been to develop a universal cross-lingual model that can withstand the changes in multi-lingual data distributions. However, most existing models assume full access to the target languages in advance, whereas in realistic scenarios this is not often the case, as new languages can be incorporated later on. In this paper, we present the Cross-lingual Lifelong Learning (CLL) challenge, where a model is continually fine-tuned to adapt to emerging data from different languages. We provide insights into what makes multilingual sequential learning particularly challenging. To surmount such challenges, we benchmark a representative set of cross-lingual continual learning algorithms and analyze their knowledge preservation, accumulation, and generalization capabilities compared to baselines on carefully curated datastreams. The implications of this analysis include a recipe for how to measure and balance between different cross-lingual continual learning desiderata, which goes beyond conventional transfer learning.