| Challenge: | a common problem with explicit ratings of translations is that users are not qualified enough to provide reliable feedback for the whole sentence. |
| Approach: | They propose a way to learn from partial feedback in neural machine translation . they ask users to highlight a correct chunk of a translation based on partial feedback . |
| Outcome: | The proposed method outperforms sentence-based feedback by 2.61% BLEU absolute. |
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Can Neural Machine Translation be Improved with User Feedback? (N18-3)
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| Challenge: | a recent study has focused on the use of explicit and implicit feedback for neural machine translation (NMT) a new study uses explicit and implied feedback to improve performance of NMT with human reinforcement. |
| Approach: | They propose to use real logged feedback to improve neural machine translation with human reinforcement. |
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Shallow-to-Deep Training for Neural Machine Translation (2020.emnlp-main)
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| Challenge: | Experimental results show that deep training is 1:4 faster than training from scratch. |
| Approach: | They propose a shallow-to-deep training method that learns deep models by stacking shallow models. |
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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. |
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Unsupervised Neural Machine Translation with Weight Sharing (P18-1)
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| Challenge: | Unsupervised neural machine translation (NMT) is a new approach for machine translation . the model uses only one shared encoder to map pairs of sentences from different languages to a shared-latent space . |
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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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A Simple and Effective Approach to Coverage-Aware Neural Machine Translation (P18-2)
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| Challenge: | Neural Machine Translation (NMT) models are used to solve translation problems using long-term models. |
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Bridging the Gap between Training and Inference for Neural Machine Translation (P19-1)
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| Challenge: | Neural Machine Translation generates target words sequentially while at inference it has to generate the entire sequence from scratch. |
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Depth Growing for Neural Machine Translation (P19-1)
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| Challenge: | Neural machine translation models with tens and even more than a hundred blocks have shown effectiveness in image recognition. |
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Revisiting Low-Resource Neural Machine Translation: A Case Study (P19-1)
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Pre-training Methods for Neural Machine Translation (2021.acl-tutorials)
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| Challenge: | This tutorial provides a comprehensive guide to make the most of pre-training for neural machine translation. |
| Approach: | This tutorial provides a comprehensive guide to make the most of pre-training for neural machine translation. |
| Outcome: | This tutorial explains how to make the most of pre-training for neural machine translation. |