Papers with document-level MT
Discourse-Centric Evaluation of Document-level Machine Translation with a New Densely Annotated Parallel Corpus of Novels (2023.acl-long)
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| Challenge: | Several recent papers claim to have achieved human parity at sentence-level machine translation. |
| Approach: | They propose to use a dataset with rich discourse annotations to evaluate MT performance . they find that MT outputs differ fundamentally from human translations in terms of latent discourse structures. |
| Outcome: | The proposed dataset builds upon the large-scale parallel corpus BWB . it covers 15,095 entity mentions in both languages and compares them to human translations . |
Non-Autoregressive Document-Level Machine Translation (2023.findings-emnlp)
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| Challenge: | Existing non-autoregressive translation models struggle with document context and handling discourse phenomena. |
| Approach: | They propose a simple but effective design of sentence alignment between source and target to improve their performance on document-level machine translation. |
| Outcome: | The proposed model achieves high acceleration on documents and sentence alignment significantly enhances their performance. |