Papers with off-target translation issue
Improving Massively Multilingual Neural Machine Translation and Zero-Shot Translation (2020.acl-main)
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| Challenge: | Existing approaches to improve multilingual neural machine translation (NMT) are weak, and lack robustness to support language pairs with varying typological characteristics. |
| Approach: | They propose to deepen NMT models to support language pairs with varying typological characteristics by random online backtranslation. |
| Outcome: | The proposed approach narrows the performance gap with bilingual models and improves zero-shot performance by 10 BLEU, approaching conventional pivot-based methods. |
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. |