| 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. |
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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. |
| 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. |
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. |
Boosting Neural Machine Translation with Similar Translations (2020.acl-main)
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| Challenge: | Statistical Machine Translation and fuzzy matching are completely different in their finality. |
| Approach: | They propose to use fuzzy matching to train neural machine translation to make use of similar translations, in a similar way a human translator employs fuzzy matches. |
| Outcome: | The proposed methods improve translation accuracy and fine-tuned model for unseen translation pairs. |
Metric for Automatic Machine Translation Evaluation based on Universal Sentence Representations (N18-4)
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| Challenge: | Sentence representations can capture information that cannot be captured by local features based on character or word Ngrams. |
| Approach: | They propose a supervised regression model using universal sentence representations capable of capturing information that cannot be captured by local features based on character or word Ngrams. |
| Outcome: | The proposed model achieves state-of-the-art performance with only sentence representation features . |
Multi-Domain Neural Machine Translation with Word-Level Domain Context Discrimination (D18-1)
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| Challenge: | Experimental results on Chinese-English and English-French multi-domain translation tasks demonstrate the effectiveness of the proposed model. |
| Approach: | They propose to use mixed-domain parallel sentences to construct a unified model that allows translation to switch between different domains. |
| Outcome: | The proposed model distinguishes and exploits word-level domain contexts on Chinese-English and English-French translation tasks. |
Filtering Back-Translated Data in Unsupervised Neural Machine Translation (2020.coling-main)
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| Challenge: | Current state of the art approaches for unsupervised neural machine translation (NMT) use only monolingual data for training. |
| Approach: | They propose an approach to filter back-translated data as part of the training process of unsupervised neural machine translation (NMT) they propose a weight component based on the quality of pseudo parallel sentence pairs generated in back-translation phase. |
| Outcome: | The proposed approach improves the training performance of unsupervised neural machine translation systems by giving weight to good pseudo parallel sentence pairs in the back-translation phase. |
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. |
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. |
| Approach: | They propose a curriculum learning approach to adapt generic neural machine translation models to a specific domain. |
| Outcome: | The proposed approach outperforms unadapted and adapted baselines in two domains and two language pairs. |
Domain Adaptation of Machine Translation with Crowdworkers (2022.emnlp-industry)
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| Challenge: | Existing methods for machine translation work well when no in-domain data are available. |
| Approach: | They propose a framework that efficiently and effectively collects parallel sentences in a target domain from the web with the help of crowdworkers. |
| Outcome: | The proposed method improves the BLEU scores by an average of +7.8 points compared to a general-purpose translation model. |
Dynamic Sentence Sampling for Efficient Training of Neural Machine Translation (P18-2)
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| Challenge: | Existing methods to train neural machine translation (NMT) use a fixed training procedure where each sentence is sampled once during each epoch. |
| Approach: | They propose to dynamically sample sentences to accelerate NMT training . a weight is assigned to each sentence based on the measured difference between training costs of two iterations. |
| Outcome: | Empirical results show that the proposed method can significantly accelerate training and improve NMT performance. |