Task Similarity and Zero-Shot Cross-Lingual Transfer Performance in Multilingual Models

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 task similarity measured via taxonomy or embeddings correlate with zero-shot performance gains in cross-lingual transfer when using multilingual models like mT5 or XLM-R?

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