| Challenge: | Neural Machine Translation models are effective when trained on broad domains with large datasets, such as news translation. |
| Approach: | They propose a novel approach for adaptive ensemble weighting for Neural Machine Translation by extending Bayesian Interpolation with source information. |
| Outcome: | The proposed approach improves performance on Spanish-English and English-German tasks without the need for the domain label. |
Similar Papers
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. |
| Outcome: | The proposed approach yields the state-of-the-art translation performance in resource rich scenarios. |
Adaptive Weighting for Neural Machine Translation (C18-1)
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| Challenge: | Existing weighted sum models (WSMs) take inputs and generate one output, but they are independent of each other and are fixed for all inputs. |
| Approach: | They propose adaptive weighting for WSMs to control the contribution of each input and output state. |
| Outcome: | The proposed weighting improves translation accuracy by 1.49 and 0.92 BLEU points on Chinese-to-English translation and English-to German translation tasks. |
Simple, Scalable Adaptation for Neural Machine Translation (D19-1)
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| Challenge: | Recent advances in deep learning have led to significantly improved quality on Neural Machine Translation (NMT) however, performance on out-of-domain data or low resource languages remains poor. |
| Approach: | They propose a simple yet efficient approach for adapting pre-trained models to multiple tasks simultaneously. |
| Outcome: | The proposed approach is on par with full fine-tuning on domain adaptation and massively multilingual NMT on a massively multilingual dataset. |
Improving the Quality Trade-Off for Neural Machine Translation Multi-Domain Adaptation (2021.emnlp-main)
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| Challenge: | Building neural machine translation systems to perform well on a specific target domain remains a challenge. |
| Approach: | They propose to train a single NMT system per language pair that performs well across multiple domains. |
| Outcome: | The proposed approach improves the Pareto frontier on this task. |
Sentence Weighting for Neural Machine Translation Domain Adaptation (C18-1)
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| Challenge: | Neural machine translation (NMT) has achieved satisfactory performance on many language pairs with various advantages over statistical machine translation. |
| Approach: | They propose a new sentence weighting method for the domain adaptation of neural machine translation that uses a domain similarity metric to evaluate the relevance of sentences to the target domain. |
| Outcome: | The proposed method achieves significant improvement over baselines on Chinese-English TED task and synthetic training task with only synthetic training parallel data. |
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. |
| Approach: | They propose an unsupervised method which fine-tunes a pre-trained out-of-domain NMT model using a pseudo-in-domain corpus. |
| Outcome: | The proposed method improves in five domains without using in-domain parallel sentences and up to 2 BLEU over strong back-translation baselines. |
Evaluating Domain Adaptation for Machine Translation Across Scenarios (L18-1)
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| Challenge: | Statistical machine translation (SMT) has been the dominant approach for the last 20 years, with neural machine translation becoming the new main paradigm in academic research and the industry. |
| Approach: | They propose to compare domain-adapted statistical and neural machine translation systems on three different domains and language pairs with varying degrees of domain specificity and available training data. |
| Outcome: | The proposed system is the best choice for translation, with marked impacts for domains with higher specificity. |
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. |
| Outcome: | The proposed techniques are more challenging yet widely applicable. |
Revisiting Multi-Domain Machine Translation (2021.tacl-1)
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| Challenge: | Existing approaches to handle multi-domain machine translation systems are lacking due to the variability of data. |
| Approach: | They propose to use domain adaptation methods to handle situations where a sample of matched sentences is available in training and where only samples of source-side sentences are available. |
| Outcome: | The proposed model is able to handle multiple domains and their expectations with respect to performance. |
Enhancing Neural Machine Translation Through Target Language Data: A kNN-LM Approach for Domain Adaptation (2025.acl-long)
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Abudurexiti Reheman, Hongyu Liu, Junhao Ruan, Abudukeyumu Abudula, Yingfeng Luo, Tong Xiao, JingBo Zhu
| Challenge: | Neural machine translation (NMT) has made significant progress in recent years, yet often suffers from translating in new domains, which is called domain adaptation. |
| Approach: | They propose a method that leverages semantically similar target language sentences in the kNN framework and generates a probability distribution over these sentences during decoding. |
| Outcome: | The proposed method generates a probability distribution over similar target language sentences and then interpolates with the model’s distribution. |