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        <title>Multi Task Learning For Zero Shot Performance Prediction of Multilingual Models</title>
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    <abstract>Massively Multilingual Transformer based Language Models have been observed to be surprisingly effective on zero-shot transfer across languages, though the performance varies from language to language depending on the pivot language(s) used for fine-tuning. In this work, we build upon some of the existing techniques for predicting the zero-shot performance on a task, by modeling it as a multi-task learning problem. We jointly train predictive models for different tasks which helps us build more accurate predictors for tasks where we have test data in very few languages to measure the actual performance of the model. Our approach also lends us the ability to perform a much more robust feature selection, and identify a common set of features that influence zero-shot performance across a variety of tasks.</abstract>
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%0 Conference Proceedings
%T Multi Task Learning For Zero Shot Performance Prediction of Multilingual Models
%A Ahuja, Kabir
%A Kumar, Shanu
%A Dandapat, Sandipan
%A Choudhury, Monojit
%Y Muresan, Smaranda
%Y Nakov, Preslav
%Y Villavicencio, Aline
%S Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2022
%8 May
%I Association for Computational Linguistics
%C Dublin, Ireland
%F ahuja-etal-2022-multi
%X Massively Multilingual Transformer based Language Models have been observed to be surprisingly effective on zero-shot transfer across languages, though the performance varies from language to language depending on the pivot language(s) used for fine-tuning. In this work, we build upon some of the existing techniques for predicting the zero-shot performance on a task, by modeling it as a multi-task learning problem. We jointly train predictive models for different tasks which helps us build more accurate predictors for tasks where we have test data in very few languages to measure the actual performance of the model. Our approach also lends us the ability to perform a much more robust feature selection, and identify a common set of features that influence zero-shot performance across a variety of tasks.
%R 10.18653/v1/2022.acl-long.374
%U https://aclanthology.org/2022.acl-long.374
%U https://doi.org/10.18653/v1/2022.acl-long.374
%P 5454-5467

Markdown (Informal)

[Multi Task Learning For Zero Shot Performance Prediction of Multilingual Models](https://aclanthology.org/2022.acl-long.374) (Ahuja et al., ACL 2022)

• Multi Task Learning For Zero Shot Performance Prediction of Multilingual Models (Ahuja et al., ACL 2022)

ACL

• Kabir Ahuja, Shanu Kumar, Sandipan Dandapat, and Monojit Choudhury. 2022. Multi Task Learning For Zero Shot Performance Prediction of Multilingual Models. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 5454–5467, Dublin, Ireland. Association for Computational Linguistics.