Discourse Graph Guided Document Translation with Large Language Models

• URL: http://arxiv.org/abs/2511.07230v1
• Date: Mon, 10 Nov 2025 15:48:01 GMT
• Title: Discourse Graph Guided Document Translation with Large Language Models
• Authors: Viet-Thanh Pham, Minghan Wang, Hao-Han Liao, Thuy-Trang Vu,
• Abstract summary: TransGraph is a discourse-guided framework that explicitly models inter-chunk relationships through structured discourse graphs.<n>It consistently surpasses strong baselines in translation quality and terminology consistency while incurring significantly lower token overhead.
• Score: 18.88786853549414
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Adapting large language models to full document translation remains challenging due to the difficulty of capturing long-range dependencies and preserving discourse coherence throughout extended texts. While recent agentic machine translation systems mitigate context window constraints through multi-agent orchestration and persistent memory, they require substantial computational resources and are sensitive to memory retrieval strategies. We introduce TransGraph, a discourse-guided framework that explicitly models inter-chunk relationships through structured discourse graphs and selectively conditions each translation segment on relevant graph neighbourhoods rather than relying on sequential or exhaustive context. Across three document-level MT benchmarks spanning six languages and diverse domains, TransGraph consistently surpasses strong baselines in translation quality and terminology consistency while incurring significantly lower token overhead.

Related papers

• ReMeREC: Relation-aware and Multi-entity Referring Expression Comprehension [29.50623143244436]
  ReMeREC aims to localize specified entities or regions in an image based on natural language descriptions.<n>We first construct a relation-aware, multi-entity REC dataset called ReMeX.<n>We then propose ReMeREC, a novel framework that jointly leverages visual and textual cues to localize multiple entities.
  arXiv  Detail & Related papers  (2025-07-22T11:23:48Z)
• GRAFT: A Graph-based Flow-aware Agentic Framework for Document-level Machine Translation [29.444855969559153]
  We propose Graph Augmented Agentic Framework for Document Level Translation (GRAFT) for document translation.<n>GRAFT integrates segmentation, directed acyclic graph (DAG) based dependency modelling, and discourse aware translation into a cohesive framework.<n>Experiments conducted across eight translation directions and six diverse domains demonstrate that GRAFT achieves significant performance gains over state of the art DocMT systems.
  arXiv  Detail & Related papers  (2025-07-04T05:45:55Z)
• Multilingual Contextualization of Large Language Models for Document-Level Machine Translation [28.08957305340726]
  Large language models (LLMs) have demonstrated strong performance in sentence-level machine translation.<n>We propose a method to improve LLM-based long-document translation through targeted fine-tuning on high-quality document-level data.<n>Our approach supports multiple translation paradigms, including direct document-to-document and chunk-level translation.
  arXiv  Detail & Related papers  (2025-04-16T14:52:22Z)
• PICASO: Permutation-Invariant Context Composition with State Space Models [98.91198288025117]
  State Space Models (SSMs) offer a promising solution by allowing a database of contexts to be mapped onto fixed-dimensional states.<n>We propose a simple mathematical relation derived from SSM dynamics to compose multiple states into one that efficiently approximates the effect of concatenating raw context tokens.<n>We evaluate our resulting method on WikiText and MSMARCO in both zero-shot and fine-tuned settings, and show that we can match the strongest performing baseline while enjoying on average 5.4x speedup.
  arXiv  Detail & Related papers  (2025-02-24T19:48:00Z)
• Text Reading Order in Uncontrolled Conditions by Sparse Graph Segmentation [71.40119152422295]
  We propose a lightweight, scalable and generalizable approach to identify text reading order. The model is language-agnostic and runs effectively across multi-language datasets. It is small enough to be deployed on virtually any platform including mobile devices.
  arXiv  Detail & Related papers  (2023-05-04T06:21:00Z)
• PARAGRAPH2GRAPH: A GNN-based framework for layout paragraph analysis [6.155943751502232]
  We present a language-independent graph neural network (GNN)-based model that achieves competitive results on common document layout datasets. Our model is suitable for industrial applications, particularly in multi-language scenarios.
  arXiv  Detail & Related papers  (2023-04-24T03:54:48Z)
• Modeling Context With Linear Attention for Scalable Document-Level Translation [72.41955536834702]
  We investigate the efficacy of a recent linear attention model on document translation and augment it with a sentential gate to promote a recency inductive bias. We show that sentential gating further improves translation quality on IWSLT.
  arXiv  Detail & Related papers  (2022-10-16T03:41:50Z)
• Unsupervised Image-to-Image Translation with Generative Prior [103.54337984566877]
  Unsupervised image-to-image translation aims to learn the translation between two visual domains without paired data. We present a novel framework, Generative Prior-guided UN Image-to-image Translation (GP-UNIT), to improve the overall quality and applicability of the translation algorithm.
  arXiv  Detail & Related papers  (2022-04-07T17:59:23Z)
• BASS: Boosting Abstractive Summarization with Unified Semantic Graph [49.48925904426591]
  BASS is a framework for Boosting Abstractive Summarization based on a unified Semantic graph. A graph-based encoder-decoder model is proposed to improve both the document representation and summary generation process. Empirical results show that the proposed architecture brings substantial improvements for both long-document and multi-document summarization tasks.
  arXiv  Detail & Related papers  (2021-05-25T16:20:48Z)
• Document Graph for Neural Machine Translation [42.13593962963306]
  We show that a document can be represented as a graph that connects relevant contexts regardless of their distances. Experiments on various NMT benchmarks, including IWSLT English-French, Chinese-English, WMT English-German and Opensubtitle English-Russian, demonstrate that using document graphs can significantly improve the translation quality.
  arXiv  Detail & Related papers  (2020-12-07T06:48:59Z)
• GATE: Graph Attention Transformer Encoder for Cross-lingual Relation and Event Extraction [107.8262586956778]
  We introduce graph convolutional networks (GCNs) with universal dependency parses to learn language-agnostic sentence representations. GCNs struggle to model words with long-range dependencies or are not directly connected in the dependency tree. We propose to utilize the self-attention mechanism to learn the dependencies between words with different syntactic distances.
  arXiv  Detail & Related papers  (2020-10-06T20:30:35Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.