| Challenge: | Existing neural machine translation models only use one correct sentence as the target, and the other correct sentences are punished as the incorrect ones. |
| Approach: | They propose an approach that uses both the sentences and the bag-of-words as targets in the training stage to encourage the model to generate the potentially correct sentences that are not appeared in the train set. |
| Outcome: | The proposed model outperforms baseline models on a Chinese-English translation dataset by the BLEU score of 4.55. |
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| Challenge: | Neural machine translation models are weak enough for document-level translation . current models only translate sentences individually, resulting in poor document coherence . |
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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. |
| Approach: | They propose to use ground truth and inference to generate target words sequentially while at inference it has to generate the entire sequence from scratch. |
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Quality Scoring of Source Words in Neural Translation Models (2022.emnlp-main)
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| Challenge: | Recent approaches to improving word-level quality scores on input source sentences require training special word-scoring models or require repeated invocation of the translation model. |
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Guiding Neural Machine Translation with Retrieved Translation Pieces (N18-1)
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| Challenge: | Neural machine translation (NMT) has trouble with lowfrequency words or phrases and generalizing across domains. |
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Back-Translation Sampling by Targeting Difficult Words in Neural Machine Translation (D18-1)
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| Challenge: | Neural machine translation (NMT) uses a sequence-to-sequence model to generate synthetic data. |
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Improving Lexical Choice in Neural Machine Translation (N18-1)
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| Challenge: | False positives: the output layer rewards frequent words disproportionately, we argue . Falsibles: a model that learns word representations in continuous space tends to translate rare words . |
| Approach: | They propose to fix the norms of both vectors to a constant value and integrate a lexical module which is jointly trained with the rest of the model. |
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Phrase-Based & Neural Unsupervised Machine Translation (D18-1)
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| Challenge: | Recent advances in machine translation have reported near human-level performance on several languages, yet their effectiveness strongly relies on the availability of large amounts of parallel sentences. |
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Encouraging Lexical Translation Consistency for Document-Level Neural Machine Translation (2021.emnlp-main)
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| Challenge: | Experimental results show document-level neural machine translation improves lexical consistency . inconsistent translations tend to confuse readers in some cases . |
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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. |
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Document Context Neural Machine Translation with Memory Networks (P18-1)
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| Challenge: | Experimental results show that our model exploits both source and target document context. |
| Approach: | They propose a document-level neural machine translation model which takes both source and target document context into account using memory networks. |
| Outcome: | The proposed model outperforms previous work in terms of BLEU and METEOR in English translations. |