@inproceedings{kharitonova-etal-2021-transfer,
title = "Transfer Learning with Shallow Decoders: {BSC} at {WMT}2021{'}s Multilingual Low-Resource Translation for {I}ndo-{E}uropean Languages Shared Task",
author = "Kharitonova, Ksenia and
de Gibert Bonet, Ona and
Armengol-Estap{\'e}, Jordi and
Rodriguez i Alvarez, Mar and
Melero, Maite",
booktitle = "Proceedings of the Sixth Conference on Machine Translation",
month = nov,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.wmt-1.43",
pages = "362--367",
abstract = "This paper describes the participation of the BSC team in the WMT2021{'}s Multilingual Low-Resource Translation for Indo-European Languages Shared Task. The system aims to solve the Subtask 2: Wikipedia cultural heritage articles, which involves translation in four Romance languages: Catalan, Italian, Occitan and Romanian. The submitted system is a multilingual semi-supervised machine translation model. It is based on a pre-trained language model, namely XLM-RoBERTa, that is later fine-tuned with parallel data obtained mostly from OPUS. Unlike other works, we only use XLM to initialize the encoder and randomly initialize a shallow decoder. The reported results are robust and perform well for all tested languages.",
}
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%0 Conference Proceedings
%T Transfer Learning with Shallow Decoders: BSC at WMT2021’s Multilingual Low-Resource Translation for Indo-European Languages Shared Task
%A Kharitonova, Ksenia
%A de Gibert Bonet, Ona
%A Armengol-Estapé, Jordi
%A Rodriguez i Alvarez, Mar
%A Melero, Maite
%S Proceedings of the Sixth Conference on Machine Translation
%D 2021
%8 November
%I Association for Computational Linguistics
%C Online
%F kharitonova-etal-2021-transfer
%X This paper describes the participation of the BSC team in the WMT2021’s Multilingual Low-Resource Translation for Indo-European Languages Shared Task. The system aims to solve the Subtask 2: Wikipedia cultural heritage articles, which involves translation in four Romance languages: Catalan, Italian, Occitan and Romanian. The submitted system is a multilingual semi-supervised machine translation model. It is based on a pre-trained language model, namely XLM-RoBERTa, that is later fine-tuned with parallel data obtained mostly from OPUS. Unlike other works, we only use XLM to initialize the encoder and randomly initialize a shallow decoder. The reported results are robust and perform well for all tested languages.
%U https://aclanthology.org/2021.wmt-1.43
%P 362-367
Markdown (Informal)
[Transfer Learning with Shallow Decoders: BSC at WMT2021’s Multilingual Low-Resource Translation for Indo-European Languages Shared Task](https://aclanthology.org/2021.wmt-1.43) (Kharitonova et al., WMT 2021)
ACL