Unsupervised Neural Machine Translation for Low-Resource Domains via Meta-Learning (2021.acl-long)
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| Challenge: | Unsupervised machine translation suffers from data-scarce domains, authors report . a meta-learning algorithm trains the model to adapt to another domain by utilizing only a small amount of training data. |
| Approach: | They propose a meta-learning algorithm that trains the model to adapt to another domain . their model surpasses a transfer learning-based approach by up to 2-3 BLEU scores . |
| Outcome: | The proposed algorithm outperforms a transfer learning-based approach by 2-3 BLEU scores . the proposed model outperformed previous models in the domain of unsupervised machine translation . |
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| Challenge: | In this paper, we propose to extend the recently introduced model-agnostic meta-learning algorithm for low-resource neural machine translation (NMT). |
| Approach: | They propose to extend the recently introduced meta-learning algorithm for low-resource neural machine translation (NMT) they frame low-Resource translation as a meta- learning problem where we learn to adapt to low-REsource languages based on multilingual high-resourced language tasks. |
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Self-Training for Unsupervised Neural Machine Translation in Unbalanced Training Data Scenarios (2021.naacl-main)
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| Challenge: | Existing methods that use monolingual corpora for translation are not suitable for low-resource languages such as Estonian. |
| Approach: | They propose unsupervised neural machine translation (UNMT) that relies on monolingual corpora to train a robust UNMT system and improve its performance. |
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From Priest to Doctor: Domain Adaptation for Low-Resource Neural Machine Translation (2025.coling-main)
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| Challenge: | Existing data for low-resource languages are limited; the languages that could most benefit from domain adaptation (DA) are the ones left behind. |
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Unsupervised Domain Adaptation for Neural Machine Translation with Domain-Aware Feature Embeddings (D19-1)
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| Challenge: | Recent studies have focused on domain adaptation for neural machine translation systems where in-domain data is scarce or nonexistent. |
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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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Neural Unsupervised Domain Adaptation in NLP—A Survey (2020.coling-main)
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| Challenge: | Deep neural networks excel at learning from labeled data, but learning from unlabeled data remains a challenge. |
| Approach: | They review neural unsupervised domain adaptation techniques which do not require labeled target domain data. |
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Distilling Multiple Domains for Neural Machine Translation (2020.emnlp-main)
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| Challenge: | Neural machine translation is a powerful tool for high-resource domains, but performance suffers when the input domain is low-resourced. |
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m^4 Adapter: Multilingual Multi-Domain Adaptation for Machine Translation with a Meta-Adapter (2022.findings-emnlp)
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| Challenge: | Multilingual neural machine translation models (MNMT) are effective on transferring knowledge between high-resource languages to low-resourced languages. |
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Universal Neural Machine Translation for Extremely Low Resource Languages (N18-1)
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| Challenge: | a novel multilingual approach to machine translation is proposed for low resource languages . the proposed approach can achieve 23 BLEU on Romanian-English WMT2016 using a tiny parallel corpus of 6k sentences compared to the 18 BLUE of strong baseline system . |
| Approach: | They propose a transfer-learning approach to share lexical and sentence representations across multiple source languages into one target language. |
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Cross-lingual Supervision Improves Unsupervised Neural Machine Translation (2021.naacl-industry)
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| Challenge: | Existing models that use only monolingual data have not been fully duplicated in the vast majority of language pairs, especially for zero-source languages. |
| Approach: | They propose to leverage the corpus from En-Fr and En-De to collectively train the translation from one language into many languages under one model. |
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