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        <title>Oolong: Investigating What Makes Transfer Learning Hard with Controlled Studies</title>
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    <abstract>When we transfer a pretrained language model to a new language, there are many axes of variation that change at once. To disentangle the impact of different factors like syntactic similarity and vocabulary similarity, we propose a set of controlled transfer studies: we systematically transform the language of the GLUE benchmark, altering one axis of crosslingual variation at a time, and then measure the resulting drops in a pretrained model’s downstream performance. We find that models can largely recover from syntactic-style shifts, but cannot recover from vocabulary misalignment and embedding matrix re-initialization, even with continued pretraining on 15 million tokens. Moreover, good-quality tokenizers in the transfer language do not make vocabulary alignment easier. Our experiments provide insights into the factors of cross-lingual transfer that researchers should most focus on when designing language transfer scenarios.</abstract>
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    <identifier type="doi">10.18653/v1/2023.emnlp-main.198</identifier>
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%0 Conference Proceedings
%T Oolong: Investigating What Makes Transfer Learning Hard with Controlled Studies
%A Wu, Zhengxuan
%A Tamkin, Alex
%A Papadimitriou, Isabel
%Y Bouamor, Houda
%Y Pino, Juan
%Y Bali, Kalika
%S Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore
%F wu-etal-2023-oolong
%X When we transfer a pretrained language model to a new language, there are many axes of variation that change at once. To disentangle the impact of different factors like syntactic similarity and vocabulary similarity, we propose a set of controlled transfer studies: we systematically transform the language of the GLUE benchmark, altering one axis of crosslingual variation at a time, and then measure the resulting drops in a pretrained model’s downstream performance. We find that models can largely recover from syntactic-style shifts, but cannot recover from vocabulary misalignment and embedding matrix re-initialization, even with continued pretraining on 15 million tokens. Moreover, good-quality tokenizers in the transfer language do not make vocabulary alignment easier. Our experiments provide insights into the factors of cross-lingual transfer that researchers should most focus on when designing language transfer scenarios.
%R 10.18653/v1/2023.emnlp-main.198
%U https://aclanthology.org/2023.emnlp-main.198
%U https://doi.org/10.18653/v1/2023.emnlp-main.198
%P 3280-3289

Markdown (Informal)

[Oolong: Investigating What Makes Transfer Learning Hard with Controlled Studies](https://aclanthology.org/2023.emnlp-main.198) (Wu et al., EMNLP 2023)

• Oolong: Investigating What Makes Transfer Learning Hard with Controlled Studies (Wu et al., EMNLP 2023)

ACL

• Zhengxuan Wu, Alex Tamkin, and Isabel Papadimitriou. 2023. Oolong: Investigating What Makes Transfer Learning Hard with Controlled Studies. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, pages 3280–3289, Singapore. Association for Computational Linguistics.