| Challenge: | Neural machine translation (NMT) requires large training corpora, which is problematic for low-resource languages. |
| Approach: | They propose to use an open-domain and an IT-domain corpora to train machine translations in English-Basque. |
| Outcome: | The proposed systems outperform OpenNMT, Moses SMT and Google Translate in English-Basque translation. |
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| Challenge: | a recent research line has managed to train both unsupervised and unsupervised machine translation systems using monolingual corpora only. |
| Approach: | They propose to use monolingual corpora to train both unsupervised and unsupervised machine translation systems. |
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A Survey of Domain Adaptation for Neural Machine Translation (C18-1)
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| Challenge: | Neural machine translation (NMT) is a deep learning based approach for machine translation. |
| Approach: | They propose to use a deep learning approach to train machine translation in scenarios where large-scale parallel corpora are available. |
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Revisiting Low-Resource Neural Machine Translation: A Case Study (P19-1)
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| Challenge: | Recent research has shown that neural machine translation models are highly data-inefficient and underperform phrase-based statistical machine translation (PBSMT) in low-resource settings. |
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Benchmarking Neural and Statistical Machine Translation on Low-Resource African Languages (2020.lrec-1)
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| Challenge: | a recent study has focused on languages where large amounts of resources are available. |
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Multilingual Neural Machine Translation (2020.coling-tutorials)
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| Challenge: | In this tutorial, we will cover the latest advances in NMT to enhance low-resource translation. |
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A fine-grained error analysis of NMT, SMT and RBMT output for English-to-Dutch (L18-1)
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| Challenge: | Since 2016, the landscape of automated translation has substantially changed with the arrival of neural machine translation (NMT). |
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NMT and PBSMT Error Analyses in English to Brazilian Portuguese Automatic Translations (2020.lrec-1)
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| Challenge: | Recent work proposes neural machine translation (NMT) for Brazilian Portuguese. |
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Efficient Neural Machine Translation for Low-Resource Languages via Exploiting Related Languages (2020.acl-srw)
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| Challenge: | Neural Machine Translation (NMT) is a rapidly advancing MT paradigm that can be used to improve machine translation for many languages. |
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| Challenge: | Recent advances in neural language modeling and multilingual training have prompted widespread adoption of machine translation (MT) technologies across an unprecedented range of world languages. |
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Unsupervised Statistical Machine Translation (D18-1)
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| Challenge: | Neural Machine Translation (NMT) systems can be trained from monolingual corpora without supervision. |
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