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        <title>Language Tokens: Simply Improving Zero-Shot Multi-Aligned Translation in Encoder-Decoder Models</title>
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        <namePart type="given">Muhammad</namePart>
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            <title>Proceedings of the 15th biennial conference of the Association for Machine Translation in the Americas (Volume 1: Research Track)</title>
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            <namePart type="given">Kevin</namePart>
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    <abstract>This paper proposes a simple and effective method to improve direct translation for the zero-shot case and when direct data is available. We modify the input tokens at both the encoder and decoder to include signals for the source and target languages. We show a performance gain when training from scratch, or finetuning a pretrained model with the proposed setup. In in-house experiments, our method shows nearly a 10.0 BLEU points difference depending on the stoppage criteria. In a WMT-based setting, we see 1.3 and 0.4 BLEU points improvement for the zero-shot setting, and when using direct data for training, respectively, while from-English performance improves by 4.17 and 0.85 BLEU points. In the low-resource setting, we see a 1.5 ∼ 1.7 point improvement when finetuning on directly translated domain data.</abstract>
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%0 Conference Proceedings
%T Language Tokens: Simply Improving Zero-Shot Multi-Aligned Translation in Encoder-Decoder Models
%A N ElNokrashy, Muhammad
%A Hendy, Amr
%A Maher, Mohamed
%A Afify, Mohamed
%A Hassan, Hany
%Y Duh, Kevin
%Y Guzmán, Francisco
%S Proceedings of the 15th biennial conference of the Association for Machine Translation in the Americas (Volume 1: Research Track)
%D 2022
%8 September
%I Association for Machine Translation in the Americas
%C Orlando, USA
%F n-elnokrashy-etal-2022-language
%X This paper proposes a simple and effective method to improve direct translation for the zero-shot case and when direct data is available. We modify the input tokens at both the encoder and decoder to include signals for the source and target languages. We show a performance gain when training from scratch, or finetuning a pretrained model with the proposed setup. In in-house experiments, our method shows nearly a 10.0 BLEU points difference depending on the stoppage criteria. In a WMT-based setting, we see 1.3 and 0.4 BLEU points improvement for the zero-shot setting, and when using direct data for training, respectively, while from-English performance improves by 4.17 and 0.85 BLEU points. In the low-resource setting, we see a 1.5 ∼ 1.7 point improvement when finetuning on directly translated domain data.
%U https://aclanthology.org/2022.amta-research.6
%P 70-82

Markdown (Informal)

[Language Tokens: Simply Improving Zero-Shot Multi-Aligned Translation in Encoder-Decoder Models](https://aclanthology.org/2022.amta-research.6) (N ElNokrashy et al., AMTA 2022)

• Language Tokens: Simply Improving Zero-Shot Multi-Aligned Translation in Encoder-Decoder Models (N ElNokrashy et al., AMTA 2022)

ACL

• Muhammad N ElNokrashy, Amr Hendy, Mohamed Maher, Mohamed Afify, and Hany Hassan. 2022. Language Tokens: Simply Improving Zero-Shot Multi-Aligned Translation in Encoder-Decoder Models. In Proceedings of the 15th biennial conference of the Association for Machine Translation in the Americas (Volume 1: Research Track), pages 70–82, Orlando, USA. Association for Machine Translation in the Americas.