| Challenge: | Empirical results show that a sentence-level agreement module can significantly improve the performance of neural machine translation (NMT) |
| Approach: | They propose a sentence-level agreement module to minimize the difference between the representation of source and target sentences. |
| Outcome: | Empirical results show the proposed agreement module significantly improves translation performance. |
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On Search Strategies for Document-Level Neural Machine Translation (2023.findings-acl)
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| Challenge: | Document-level neural machine translation models produce a more consistent output across a document . however, the exact decoding strategy is often not described and not mentioned at all. |
| Approach: | They propose to use standard automatic metrics and specific linguistic phenomena to compare different decoding schemes. |
| Outcome: | The proposed decoding strategies perform similar to each other on three standard document-level translation benchmarks. |
Corpora for Document-Level Neural Machine Translation (2020.lrec-1)
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| Challenge: | Document-level machine translation models translate sentences in isolation, but there are three main problems for document-level models. |
| Approach: | They propose to use document-level machine translation to capture discourse dependencies across sentences by considering a document as a whole. |
| Outcome: | The proposed method captures discourse dependencies across sentences by considering a document as a whole. |
Using Context in Neural Machine Translation Training Objectives (2020.acl-main)
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| Challenge: | Neural Machine Translation (NMT) training is based on document-level metrics, not sentence-level BLEU. |
| Approach: | They propose to merge document-level metrics with batch-level documents to improve NMT training. |
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Improving Neural Machine Translation with Neural Syntactic Distance (N19-1)
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| Challenge: | Neural syntactic distance (NSD) is used to represent constituent trees using a sequence whose length is identical to the number of words in the sentence. |
| Approach: | They propose five strategies to improve NMT with explicit use of syntactic information . et al., 2014) propose a set of five strategies that incorporate syntastic information into the encoder and/or decoder of the baseline model. |
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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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Towards Personalised and Document-level Machine Translation of Dialogue (2021.eacl-srw)
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| Challenge: | State-of-the-art (SOTA) neural machine translation systems translate texts at sentence level, ignoring context. |
| Approach: | They propose to integrate extra-textual information into the translation process for the domain of dialogue extracted from TV subtitles in five languages: English, Brazilian Portuguese, German, French and Polish. |
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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. |
| Outcome: | Experiments on Chinese->English and WMT’14 English->German translation tasks show that the proposed model can achieve significant improvements on multiple datasets. |
Prevent the Language Model from being Overconfident in Neural Machine Translation (2021.acl-long)
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| Challenge: | Neural Machine Translation models are based on partial translation and a language model that predicts the next token based only on partial. |
| Approach: | They propose a Margin-based Token-level Objective and a Sentence-level Goal to maximize the Margin . they propose to model the next token based on partial 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. |
| Approach: | They propose a method to seek a better balance between model confidence and length preference for Neural Machine Translation. |
| Outcome: | The proposed model improves on Chinese-English and English-German translation tasks. |
One Sentence One Model for Neural Machine Translation (L18-1)
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| Challenge: | Neural machine translation (NMT) is a new state of the art that can produce better results than traditional statistical machine translation. |
| Approach: | They propose a dynamic neural network which learns a general network as usual and fine-tunes it for each test sentence. |
| Outcome: | The proposed method improves translation performance when similar sentences are available. |