| 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. |
Similar Papers
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. |
| Approach: | They will cover the latest advances in NMT approaches that leverage multilingualism . they will focus on topics such as language divergence, transfer learning and pivoting . |
| Outcome: | This tutorial will cover the latest advances in NMT to enhance low-resource translation models. |
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. |
Vocabulary Adaptation for Domain Adaptation in Neural Machine Translation (2020.findings-emnlp)
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| Challenge: | Neural network methods exhibit strong performance only in a few resource-rich domains. |
| Approach: | They propose a method that fine-tunes embedding layers of a pre-trained NMT model to the target domain. |
| Outcome: | The proposed method improves fine-tuning performance in En-Ja and De-En translation by 3.86 and 3.28 BLEU points. |
Massively Multilingual Neural Machine Translation (N19-1)
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| Challenge: | Multilingual Neural Machine Translation models support translation from multiple source languages into multiple target languages. |
| Approach: | They perform extensive experiments in training massively multilingual NMT models involving up to 103 distinct languages and 204 translation directions simultaneously. |
| Outcome: | The proposed model outperforms the state-of-the-art in low resource settings while supporting up to 59 languages in 116 translation directions. |
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. |
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. |
| 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. |
Beyond Decoder-only: Large Language Models Can be Good Encoders for Machine Translation (2025.findings-acl)
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Yingfeng Luo, Tong Zheng, Yongyu Mu, Bei Li, Qinghong Zhang, Yongqi Gao, Ziqiang Xu, Peinan Feng, Xiaoqian Liu, Tong Xiao, JingBo Zhu
| Challenge: | Recent advances in machine translation have focused on a single pre-trained decoder . encoder-decoder architectures have received relatively little attention in NMT . |
| Approach: | They propose a method that leverages LLMs as MT encoders and pairs them with lightweight decoders to develop universal translation models. |
| Outcome: | The proposed method matches or surpasses baselines in terms of translation quality but achieves 75% reduction in memory footprint of the KV cache. |
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. |
Contextual Parameter Generation for Universal Neural Machine Translation (D18-1)
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| Challenge: | Existing approaches to multilingual neural machine translation lack language-specific parameterization. |
| Approach: | They propose a modification to existing neural machine translation models that allows for language specific parameterization and domain adaptation. |
| Outcome: | The proposed model surpasses state-of-the-art for both the IWSLT-15 and IWSTL-17 datasets and can perform zero-shot translation. |
Generalised Unsupervised Domain Adaptation of Neural Machine Translation with Cross-Lingual Data Selection (2021.emnlp-main)
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| Challenge: | Existing work on unsupervised domain adaptation of neural machine translation assumes access to monolingual text in either the source or target language in the new domain. |
| Approach: | They propose a method to extract in-domain sentences from a large generic monolingual corpus from 'missing' text. |
| Outcome: | The proposed method outperforms baselines up to +1.5 BLEU score on five diverse domains in three language pairs and a real-world translation scenario. |