Papers with multilingual neural machine translation model
Japanese-Russian TMU Neural Machine Translation System using Multilingual Model for WAT 2019 (D19-52)
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| Challenge: | Using parallel corpora of different language pairs as training data is effective for multilingual neural machine translation model in extremely low resource situations. |
| Approach: | They propose to use Japanese-English and English-Russian parallel corpora as training data for their system to improve JapaneseRussian news translation. |
| Outcome: | The proposed system improves translation quality for JapaneseRussian language pairs in low resource situations. |
Many-to-English Machine Translation Tools, Data, and Pretrained Models (2021.acl-demo)
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| Challenge: | Commercial translation systems support only one hundred languages or fewer . commercial translation systems do not make these models available for transfer to low resource languages . |
| Approach: | They propose a multilingual neural machine translation model that can translate from 500 source languages to English. |
| Outcome: | The proposed model can translate from 500 source languages to English, or be used as a parent model for low-resource languages. |
Informative Language Representation Learning for Massively Multilingual Neural Machine Translation (2022.coling-1)
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| Challenge: | Existing studies show that prepending language tokens fail to guide translation into right directions, especially on zero-shot translation. |
| Approach: | They propose to use language embedding embodiment and language-aware multi-head attention to learn informative language representations to channel translation into right directions. |
| Outcome: | The proposed methods improve translation direction guidance and significantly alleviate off-target translation issues on two datasets. |
Revisiting Modularized Multilingual NMT to Meet Industrial Demands (2020.emnlp-main)
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| Challenge: | Currently, the complete sharing of parameters for multilingual translation (1-1) is the most popular approach because of its compactness. |
| Approach: | They propose to use a multilingual neural machine translation model that only shares modules among the same languages as 1-1 to satisfy industrial requirements. |
| Outcome: | The proposed model can enjoy the benefits of multi-way training without the capacity bottleneck and low maintainability. |
Is Robustness Transferable across Languages in Multilingual Neural Machine Translation? (2023.findings-emnlp)
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| Challenge: | Existing studies have focused on bilingual machine translation with a single translation direction. |
| Approach: | They propose a robustness transfer analysis protocol to analyze the transferability of robustness across different languages in multilingual neural machine translation. |
| Outcome: | The proposed protocol shows that the robustness gained in one translation direction can transfer to other translation directions. |