Papers with document translation
Discourse Graph Guided Document Translation with Large Language Models (2026.eacl-long)
Copied to clipboard
| Challenge: | Recent agentic machine translation systems mitigate context window constraints but require substantial computational resources and are sensitive to memory retrieval strategies. |
| Approach: | They propose a framework that explicitly models inter-chunk relationships through structured discourse graphs and selectively conditions each translation segment on relevant graph neighbourhoods rather than sequential or exhaustive context. |
| Outcome: | The proposed framework surpasses strong baselines in translation quality and terminology consistency while incurring significantly lower token overhead. |
Rethinking Document-level Neural Machine Translation (2022.findings-acl)
Copied to clipboard
| 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 . |
| Outcome: | The proposed model outperforms sentence-level models on nine datasets and two sentence- level datasets across six languages. |
Modeling Context With Linear Attention for Scalable Document-Level Translation (2022.findings-emnlp)
Copied to clipboard
| Challenge: | Document-level machine translation models lack quadratic complexity in the sequence length due to their attention layers. |
| Approach: | They evaluate a recent linear attention model with a sentential gate to promote a recency inductive bias and compare it to open-source document translation. |
| Outcome: | The proposed model significantly improves translation quality on IWSLT 2015 and OpenSubtitles 2018 with similar or better BLEU scores. |
Scaling Law for Document Neural Machine Translation (2023.findings-emnlp)
Copied to clipboard
| Challenge: | Neural machine translation (NMT) methods fail to capture discourse phenomena such as pronominal anaphora, lexical consistency, and document coherence as the input text exceeds a single sentence. |
| Approach: | They examine the effects of model scale, data scale, and sequence length on translation quality when model size is limited. |
| Outcome: | The proposed model scales and data scales are compared with the existing models and show that increasing sequence length improves translation quality when model size is limited. |
Enhancing Large Language Models for Document-Level Translation Post-Editing Using Monolingual Data (2025.coling-main)
Copied to clipboard
| Challenge: | Large Language Models (LLMs) have excellent performance in many tasks, but they still face challenges in document translation. |
| Approach: | They propose a method that leverages the capabilities of Large Language Models to optimize document translation using only monolingual data. |
| Outcome: | The proposed method improves translation quality and improves contextual consistency in document translation using only monolingual data. |
Document Translation vs. Query Translation for Cross-Lingual Information Retrieval in the Medical Domain (2020.acl-main)
Copied to clipboard
| Challenge: | Existing studies of document translation and query translation are outdated and do not reflect the current advances in machine translation. |
| Approach: | They compare document translation and query translation approaches to cross-lingual information retrieval . they exploit Statistical Machine Translation and Neural Machine Translation paradigms to translate queries into English and English . |
| Outcome: | The proposed approach outperforms the DT approach in translation quality and retrieval quality. |
From Chaotic OCR Words to Coherent Document: A Fine-to-Coarse Zoom-Out Network for Complex-Layout Document Image Translation (2025.coling-main)
Copied to clipboard
| Challenge: | Document Image Translation (DIT) aims to translate documents in images from one language to another. |
| Approach: | They propose a novel end-to-end network called Zoom-out DIT to improve document translation by combining word positioning, sentence recognition and document organization. |
| Outcome: | The proposed network improves word positioning, sentence recognition and document organization, and improves translation quality. |
CORAL: Adaptive Retrieval Loop for Culturally-Aligned Multilingual RAG (2026.findings-acl)
Copied to clipboard
| Challenge: | Multilingual retrieval-augmented generation is inadequate for culturally grounded queries . Across two cultural QA benchmarks, CORAL achieves a 3.58%p accuracy improvement on low-resource languages . |
| Approach: | They propose a multilingual retrieval-augmented generation approach that enables iterative refinement of both the retrieval space and the retrieving probe based on the quality of the evidence. |
| Outcome: | Using CORAL, researchers find that culturally grounded queries can be improved . if retrieved documents are insufficient, the system reselects them and rewrites the query . |