Analyzing the Effect of Linguistic Similarity on Cross-Lingual Transfer: Tasks and Experimental Setups Matter

• URL: http://arxiv.org/abs/2501.14491v1
• Date: Fri, 24 Jan 2025 13:48:10 GMT
• Title: Analyzing the Effect of Linguistic Similarity on Cross-Lingual Transfer: Tasks and Experimental Setups Matter
• Authors: Verena Blaschke, Masha Fedzechkina, Maartje ter Hoeve,
• Abstract summary: Cross-lingual transfer is a popular approach to increase the amount of training data for NLP tasks in a low-resource context. In this work, we analyze cross-lingual transfer for 266 languages from a wide variety of language families.
• Score: 5.237387600625361
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• Abstract: Cross-lingual transfer is a popular approach to increase the amount of training data for NLP tasks in a low-resource context. However, the best strategy to decide which cross-lingual data to include is unclear. Prior research often focuses on a small set of languages from a few language families and/or a single task. It is still an open question how these findings extend to a wider variety of languages and tasks. In this work, we analyze cross-lingual transfer for 266 languages from a wide variety of language families. Moreover, we include three popular NLP tasks: POS tagging, dependency parsing, and topic classification. Our findings indicate that the effect of linguistic similarity on transfer performance depends on a range of factors: the NLP task, the (mono- or multilingual) input representations, and the definition of linguistic similarity.

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