Papers with document-level machine translation
Document-Level Neural Machine Translation Using BERT as Context Encoder (2020.aacl-srw)
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| Challenge: | Large-scale pre-trained representations such as BERT have been widely used in many natural language understanding tasks. |
| Approach: | They propose to use BERT as a context encoder to achieve document-level contextual information, which is then integrated into both the encoder and decoder. |
| Outcome: | The proposed model outperforms strong document-level machine translation baselines on BLEU score and captures document- level context information to boost translation performance. |
Learn To Remember: Transformer with Recurrent Memory for Document-Level Machine Translation (2022.findings-naacl)
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| Challenge: | Recent studies have shown that the effective use of contextual information between sentences can achieve better performance in document-level machine translation. |
| Approach: | They propose a recurrent memory unit to the Transformer to support the information exchange between the sentence and previous context. |
| Outcome: | The proposed model outperforms the previous work on TED and News by 0.91 s-BLEU and 1.49 d-BLUE on average. |
DiscoScore: Evaluating Text Generation with BERT and Discourse Coherence (2023.eacl-main)
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| Challenge: | DiscoScore is a parametrized discourse metric that uses BERT to model discourse coherence . it is weak when operated at system level, and is therefore not reliable in a way to spot improvements . |
| Approach: | They propose a parametrized discourse metric which uses BERT to model discourse coherence from different perspectives. |
| Outcome: | The proposed model outperforms existing models on document-level machine translation and summarization. |
A Simple and Effective Unified Encoder for Document-Level Machine Translation (2020.acl-main)
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| Challenge: | Existing models for document-level machine translation use two separate encoders to model the source sentences and document- level contexts. |
| Approach: | They propose a unified encoder that can outperform existing models of dual-encoder models . they propose to use document-level contexts to model the interaction between the contexts and the source sentences . |
| Outcome: | The proposed model outperforms baseline models of dual-encoder models in terms of BLEU and METEOR scores. |
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. |
Document-Level Machine Translation with Large-Scale Public Parallel Corpora (2024.acl-long)
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| 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)
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| 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. |
Exploring Discourse Structure in Document-level Machine Translation (2023.emnlp-main)
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| Challenge: | Existing methods for document-level machine translation (DocMT) are under-utilizing the context. |
| Approach: | They propose a paragraph-to-paragraph translation mode that utilizes discourse information . they propose 'speech-based' translation mode which utilizes contextual information based on the context . |
| Outcome: | The proposed method utilizes discourse information and performs better than previous methods. |
Document-Level Machine Translation with Large Language Models (2023.emnlp-main)
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| Challenge: | Large language models (LLMs) such as ChatGPT can produce coherent, cohesive, relevant, and fluent answers for various natural language processing tasks. |
| Approach: | They examine the impact of different prompts on document-level translation quality and discourse phenomena using figures and lines, which are invisible to GPT-4. |
| Outcome: | The proposed models outperform commercial MT systems and advanced document-level MT methods on a number of benchmarks and show potential to become a new paradigm for document- level translation. |
Source-primed Multi-turn Conversation Helps Large Language Models Translate Documents (2025.findings-emnlp)
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| Challenge: | Document-level machine translations have paved the way for truly simple document-level translation, but challenges such as omission errors remain. |
| Approach: | They propose a method for document-level machine translation that leverages previous contexts in a multi-turn conversational manner by decomposing documents into segments and iteratively translating them while maintaining previous turns. |
| Outcome: | The proposed method outperforms translations of entire documents in a single turn and translations independently according to multiple automatic metrics in representative LLMs. |
Document-Level Text Generation with Minimum Bayes Risk Decoding using Optimal Transport (2025.acl-long)
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| Challenge: | Document-level text generation tasks are more difficult than sentence-level ones as they require an understanding of longer context to generate high-quality texts. |
| Approach: | They propose a variant of minimum bayes risk decoding that uses a sentence-level utility function to compute the utility of a document. |
| Outcome: | The proposed decoding outperforms the standard MBR in document-level tasks. |