@inproceedings{thakkar-etal-2021-sequence,
title = "[RETRACTED] Sequence Mixup for Zero-Shot Cross-Lingual Part-Of-Speech Tagging",
author = "Thakkar, Megh and
Shah, Vishwa and
Sawhney, Ramit and
Mukherjee, Debdoot",
editor = "Ataman, Duygu and
Birch, Alexandra and
Conneau, Alexis and
Firat, Orhan and
Ruder, Sebastian and
Sahin, Gozde Gul",
booktitle = "Proceedings of the 1st Workshop on Multilingual Representation Learning",
month = nov,
year = "2021",
address = "Punta Cana, Dominican Republic",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.mrl-1.22",
doi = "10.18653/v1/2021.mrl-1.22",
pages = "245--247",
abstract = "There have been efforts in cross-lingual transfer learning for various tasks. We present an approach utilizing an interpolative data augmentation method, Mixup, to improve the generalizability of models for part-of-speech tagging trained on a source language, improving its performance on unseen target languages. Through experiments on ten languages with diverse structures and language roots, we put forward its applicability for downstream zero-shot cross-lingual tasks.",
}
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<abstract>There have been efforts in cross-lingual transfer learning for various tasks. We present an approach utilizing an interpolative data augmentation method, Mixup, to improve the generalizability of models for part-of-speech tagging trained on a source language, improving its performance on unseen target languages. Through experiments on ten languages with diverse structures and language roots, we put forward its applicability for downstream zero-shot cross-lingual tasks.</abstract>
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%0 Conference Proceedings
%T [RETRACTED] Sequence Mixup for Zero-Shot Cross-Lingual Part-Of-Speech Tagging
%A Thakkar, Megh
%A Shah, Vishwa
%A Sawhney, Ramit
%A Mukherjee, Debdoot
%Y Ataman, Duygu
%Y Birch, Alexandra
%Y Conneau, Alexis
%Y Firat, Orhan
%Y Ruder, Sebastian
%Y Sahin, Gozde Gul
%S Proceedings of the 1st Workshop on Multilingual Representation Learning
%D 2021
%8 November
%I Association for Computational Linguistics
%C Punta Cana, Dominican Republic
%F thakkar-etal-2021-sequence
%X There have been efforts in cross-lingual transfer learning for various tasks. We present an approach utilizing an interpolative data augmentation method, Mixup, to improve the generalizability of models for part-of-speech tagging trained on a source language, improving its performance on unseen target languages. Through experiments on ten languages with diverse structures and language roots, we put forward its applicability for downstream zero-shot cross-lingual tasks.
%R 10.18653/v1/2021.mrl-1.22
%U https://aclanthology.org/2021.mrl-1.22
%U https://doi.org/10.18653/v1/2021.mrl-1.22
%P 245-247
Markdown (Informal)
[Sequence Mixup for Zero-Shot Cross-Lingual Part-Of-Speech Tagging](https://aclanthology.org/2021.mrl-1.22) (Thakkar et al., MRL 2021)
ACL