| 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. |
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| Challenge: | Document-level machine translation (MT) remains challenging due to the difficulty in efficiently using document context. |
| Approach: | They propose a hierarchical model to learn document context for document-level neural machine translation . they use a sentence encoder to capture intra-sentence dependencies and a document encoder . |
| Outcome: | The proposed model significantly improves document-level translation performance over strong baselines. |
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. |
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When and Why is Document-level Context Useful in Neural Machine Translation? (D19-65)
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| Challenge: | Recent advances in document-level NMT focus on sophisticated integration of the context, explaining its improvement with only a few selected examples or targeted test sets. |
| Approach: | They extensively quantify the causes of improvements by a document-level model in general test sets, clarifying the limit of the usefulness of document- level context in NMT. |
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Document Graph for Neural Machine Translation (2021.emnlp-main)
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| Challenge: | Existing document-level NMT methods fail to leverage contexts beyond a few set of previous sentences. |
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Breaking the Corpus Bottleneck for Context-Aware Neural Machine Translation with Cross-Task Pre-training (2021.acl-long)
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| Challenge: | Context-aware neural machine translation (NMT) remains challenging due to the lack of large-scale document-level parallel corpora. |
| Approach: | They propose to use large-scale parallel datasets and source-side monolingual documents to improve context-aware neural machine translation. |
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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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Rethinking Document-level Neural Machine Translation (2022.findings-acl)
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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 . |
| Approach: | They propose to use the original Transformer model to test document-level neural machine translation . they find that the original transformer models can achieve strong results for document translation if trained properly . |
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TANDO: A Corpus for Document-level Machine Translation (2022.lrec-1)
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Harritxu Gete, Thierry Etchegoyhen, David Ponce, Gorka Labaka, Nora Aranberri, Ander Corral, Xabier Saralegi, Igor Ellakuria, Maite Martin
| Challenge: | Document-level Neural Machine Translation aims to increase the quality of neural translation models by taking into account contextual information. |
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Improving the Transformer Translation Model with Document-Level Context (D18-1)
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| Challenge: | Existing models for document-level context translation ignore documentlevel context. |
| Approach: | They propose a document-level context encoder to represent document- level context and integrate it into the Transformer model. |
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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. |
| 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. |