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        <title>MALM: Mixing Augmented Language Modeling for Zero-Shot Machine Translation</title>
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            <namePart type="given">Mika</namePart>
            <namePart type="family">Hämäläinen</namePart>
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            <namePart type="given">Khalid</namePart>
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    <abstract>Large pre-trained language models have brought remarkable progress in NLP. Pre-training and Fine-tuning have given state-of-art performance across tasks in text processing. Data Augmentation techniques have also helped build state-of-art models on low or zero resource tasks. Many works in the past have attempted at learning a single massively multilingual machine translation model for zero-shot translation. Although those translation models are producing correct translations, the main challenge is those models are producing the wrong languages for zero-shot translation. This work and its results indicate that prompt conditioned large models do not suffer from off-target language errors i.e. errors arising due to translation to wrong languages. We empirically demonstrate the effectiveness of self-supervised pre-training and data augmentation for zero-shot multi-lingual machine translation.</abstract>
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    <identifier type="doi">10.18653/v1/2022.nlp4dh-1.8</identifier>
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%0 Conference Proceedings
%T MALM: Mixing Augmented Language Modeling for Zero-Shot Machine Translation
%A Gupta, Kshitij
%Y Hämäläinen, Mika
%Y Alnajjar, Khalid
%Y Partanen, Niko
%Y Rueter, Jack
%S Proceedings of the 2nd International Workshop on Natural Language Processing for Digital Humanities
%D 2022
%8 November
%I Association for Computational Linguistics
%C Taipei, Taiwan
%F gupta-2022-malm
%X Large pre-trained language models have brought remarkable progress in NLP. Pre-training and Fine-tuning have given state-of-art performance across tasks in text processing. Data Augmentation techniques have also helped build state-of-art models on low or zero resource tasks. Many works in the past have attempted at learning a single massively multilingual machine translation model for zero-shot translation. Although those translation models are producing correct translations, the main challenge is those models are producing the wrong languages for zero-shot translation. This work and its results indicate that prompt conditioned large models do not suffer from off-target language errors i.e. errors arising due to translation to wrong languages. We empirically demonstrate the effectiveness of self-supervised pre-training and data augmentation for zero-shot multi-lingual machine translation.
%R 10.18653/v1/2022.nlp4dh-1.8
%U https://aclanthology.org/2022.nlp4dh-1.8
%U https://doi.org/10.18653/v1/2022.nlp4dh-1.8
%P 53-58

Markdown (Informal)

[MALM: Mixing Augmented Language Modeling for Zero-Shot Machine Translation](https://aclanthology.org/2022.nlp4dh-1.8) (Gupta, NLP4DH 2022)

• MALM: Mixing Augmented Language Modeling for Zero-Shot Machine Translation (Gupta, NLP4DH 2022)

ACL

• Kshitij Gupta. 2022. MALM: Mixing Augmented Language Modeling for Zero-Shot Machine Translation. In Proceedings of the 2nd International Workshop on Natural Language Processing for Digital Humanities, pages 53–58, Taipei, Taiwan. Association for Computational Linguistics.