Cross-lingual switch is a well-liked method to extend the quantity of coaching knowledge for NLP duties in a low-resource context. Nevertheless, the most effective technique to determine which cross-lingual knowledge to incorporate is unclear. Prior analysis usually focuses on a small set of languages from just a few language households or a single activity. It’s nonetheless an open query how these findings lengthen to a greater diversity of languages and duties. On this work, we contribute to this query by analyzing cross-lingual switch for 263 languages from all kinds of language households. Furthermore, we embody three fashionable NLP duties in our evaluation: POS-tagging, dependency parsing, and matter classification. Our findings point out that the impact of linguistic similarity on switch efficiency is dependent upon a spread of things: the NLP activity, the (mono- or multilingual) enter representations, and the definition of linguistic similarity.
- † Work executed whereas at Apple
- ‡ LMU Munich
- § Munich Middle for Machine Studying
