Untangling the Influence of Typology, Data and Model Architecture on Ranking Transfer Languages for Cross-Lingual POS Tagging

• URL: http://arxiv.org/abs/2503.19979v1
• Date: Tue, 25 Mar 2025 18:05:40 GMT
• Title: Untangling the Influence of Typology, Data and Model Architecture on Ranking Transfer Languages for Cross-Lingual POS Tagging
• Authors: Enora Rice, Ali Marashian, Hannah Haynie, Katharina von der Wense, Alexis Palmer,
• Abstract summary: Cross-lingual transfer learning is an invaluable tool for overcoming data scarcity.<n>The precise roles of linguistic typology, training data, and model architecture in transfer language choice are not fully understood.<n>We take a holistic approach, examining how both dataset-specific and fine-grained typological features influence transfer language selection.
• Score: 1.743739675178893
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Cross-lingual transfer learning is an invaluable tool for overcoming data scarcity, yet selecting a suitable transfer language remains a challenge. The precise roles of linguistic typology, training data, and model architecture in transfer language choice are not fully understood. We take a holistic approach, examining how both dataset-specific and fine-grained typological features influence transfer language selection for part-of-speech tagging, considering two different sources for morphosyntactic features. While previous work examines these dynamics in the context of bilingual biLSTMS, we extend our analysis to a more modern transfer learning pipeline: zero-shot prediction with pretrained multilingual models. We train a series of transfer language ranking systems and examine how different feature inputs influence ranker performance across architectures. Word overlap, type-token ratio, and genealogical distance emerge as top features across all architectures. Our findings reveal that a combination of typological and dataset-dependent features leads to the best rankings, and that good performance can be obtained with either feature group on its own.

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