Intermediate Task Diversity and Zero-Shot Performance in Low-Resource Languages within the XTREME Benchmark

Abstract

Abstract: While natural language processing systems often focus on a single language, multilingual transfer learning has the potential to improve performance, especially for low-resource languages. We introduce XLDA, cross-lingual data augmentation, a method that replaces a segment of the input text with its translation in another language. XLDA enhances performance of all 14 tested languages of the cross-lingual natural language inference (XNLI) benchmark. With improvements of up to \$4.8\%\$, training with XLDA achieves state-of-the-art performance for Greek, Turkish, and Urdu. XLDA is in contrast to, a

Research Question

What is the impact of varying the diversity of intermediate language-understanding tasks (e.g., combining span extraction, natural language inference, and question answering) on zero-shot performance in low-resource languages in XTREME?

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