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        <title>Multilingual Translation via Grafting Pre-trained Language Models</title>
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        <namePart type="given">Zewei</namePart>
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        <namePart type="given">Mingxuan</namePart>
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            <title>Findings of the Association for Computational Linguistics: EMNLP 2021</title>
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        <name type="personal">
            <namePart type="given">Marie-Francine</namePart>
            <namePart type="family">Moens</namePart>
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        <name type="personal">
            <namePart type="given">Xuanjing</namePart>
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            <namePart type="given">Lucia</namePart>
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    <abstract>Can pre-trained BERT for one language and GPT for another be glued together to translate texts? Self-supervised training using only monolingual data has led to the success of pre-trained (masked) language models in many NLP tasks. However, directly connecting BERT as an encoder and GPT as a decoder can be challenging in machine translation, for GPT-like models lack a cross-attention component that is needed in seq2seq decoders. In this paper, we propose Graformer to graft separately pre-trained (masked) language models for machine translation. With monolingual data for pre-training and parallel data for grafting training, we maximally take advantage of the usage of both types of data. Experiments on 60 directions show that our method achieves average improvements of 5.8 BLEU in x2en and 2.9 BLEU in en2x directions comparing with the multilingual Transformer of the same size.</abstract>
    <identifier type="citekey">sun-etal-2021-multilingual-translation</identifier>
    <identifier type="doi">10.18653/v1/2021.findings-emnlp.233</identifier>
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%0 Conference Proceedings
%T Multilingual Translation via Grafting Pre-trained Language Models
%A Sun, Zewei
%A Wang, Mingxuan
%A Li, Lei
%Y Moens, Marie-Francine
%Y Huang, Xuanjing
%Y Specia, Lucia
%Y Yih, Scott Wen-tau
%S Findings of the Association for Computational Linguistics: EMNLP 2021
%D 2021
%8 November
%I Association for Computational Linguistics
%C Punta Cana, Dominican Republic
%F sun-etal-2021-multilingual-translation
%X Can pre-trained BERT for one language and GPT for another be glued together to translate texts? Self-supervised training using only monolingual data has led to the success of pre-trained (masked) language models in many NLP tasks. However, directly connecting BERT as an encoder and GPT as a decoder can be challenging in machine translation, for GPT-like models lack a cross-attention component that is needed in seq2seq decoders. In this paper, we propose Graformer to graft separately pre-trained (masked) language models for machine translation. With monolingual data for pre-training and parallel data for grafting training, we maximally take advantage of the usage of both types of data. Experiments on 60 directions show that our method achieves average improvements of 5.8 BLEU in x2en and 2.9 BLEU in en2x directions comparing with the multilingual Transformer of the same size.
%R 10.18653/v1/2021.findings-emnlp.233
%U https://aclanthology.org/2021.findings-emnlp.233
%U https://doi.org/10.18653/v1/2021.findings-emnlp.233
%P 2735-2747

Markdown (Informal)

[Multilingual Translation via Grafting Pre-trained Language Models](https://aclanthology.org/2021.findings-emnlp.233) (Sun et al., Findings 2021)

• Multilingual Translation via Grafting Pre-trained Language Models (Sun et al., Findings 2021)

ACL

• Zewei Sun, Mingxuan Wang, and Lei Li. 2021. Multilingual Translation via Grafting Pre-trained Language Models. In Findings of the Association for Computational Linguistics: EMNLP 2021, pages 2735–2747, Punta Cana, Dominican Republic. Association for Computational Linguistics.