Intermediate Task Difficulty and Zero-Shot Cross-Lingual Transfer in XTREME-R

Abstract

Abstract: In zero-shot cross-lingual transfer, a supervised NLP task trained on a corpus in one language is directly applicable to another language without any additional training. A source of cross-lingual transfer can be as straightforward as lexical overlap between languages (e.g., use of the same scripts, shared subwords) that naturally forces text embeddings to occupy a similar representation space. Recently introduced cross-lingual language model (XLM) pretraining brings out neural parameter sharing in Transformer-style networks as the most important factor for the transfer. In this paper, we aim

Research Question

Does the choice of intermediate task difficulty (e.g., task complexity measured by accuracy on English benchmarks) influence zero-shot cross-lingual transfer performance in XTREME-R?

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