Papers with sentence-level machine translation
Discourse-Centric Evaluation of Document-level Machine Translation with a New Densely Annotated Parallel Corpus of Novels (2023.acl-long)
Copied to clipboard
| 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 . |
Document-Level Machine Translation with Large-Scale Public Parallel Corpora (2024.acl-long)
Copied to clipboard
| Challenge: | Document-level machine translation has inherent advantages over sentence-level translation due to additional information available to a model from document context. |
| Approach: | They propose to use document context to train context-aware models on these datasets and to use it to model document-level phenomena. |
| Outcome: | The proposed models improve translation quality and target document-level phenomena by incorporating contextual information from several preceding sentences. |
SubDocTrans: Enhancing Document-level Machine Translation with Plug-and-play Multi-granularity Knowledge Augmentation (2025.findings-emnlp)
Copied to clipboard
| Challenge: | Document translations generated by large language models suffer from poor consistency, weak coherence, and omission errors. |
| Approach: | They propose a document-level machine translation framework that extracts knowledge from documents to produce high-quality translations. |
| Outcome: | The proposed framework improves consistency and coherence, reduces omission errors, and mitigates hallucinations. |
Non-Autoregressive Document-Level Machine Translation (2023.findings-emnlp)
Copied to clipboard
| 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. |