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. |
| Approach: | They propose an approach that adapts models with domain-aware feature embeddings, which are learned via an auxiliary language modeling task. |
| Outcome: | The proposed model performs better in multiple experimental settings and with back translation. |
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| Challenge: | Deep neural networks excel at learning from labeled data, but learning from unlabeled data remains a challenge. |
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Domain Adaptation of Neural Machine Translation by Lexicon Induction (P19-1)
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| Challenge: | Neural machine translation (NMT) is sensitive to domain shift, resulting in failure for sentences with large numbers of unknown words and lack of supervision for domain-specific words. |
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Generalised Unsupervised Domain Adaptation of Neural Machine Translation with Cross-Lingual Data Selection (2021.emnlp-main)
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Integrating Domain Terminology into Neural Machine Translation (2020.coling-main)
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| Challenge: | Existing work on terminology integration into Neural Machine Translation shows it can dynamically specialize translation to a specific domain. |
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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. |
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Domain Adaptive Inference for Neural Machine Translation (P19-1)
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| Challenge: | Neural Machine Translation models are effective when trained on broad domains with large datasets, such as news translation. |
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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. |
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Curriculum Learning for Domain Adaptation in Neural Machine Translation (N19-1)
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| Challenge: | Neural machine translation (NMT) performance drops when domains do not match and in-domain training data is scarce. |
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Improving Domain Adaptation Translation with Domain Invariant and Specific Information (N19-1)
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| Challenge: | Neural machine translation models are based on the encoder-decoder architecture, which makes them overfitting to frequent observations. |
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