G^2C-MT: Graph-Guided Context Selection for Document-Level Machine Translation

• URL: http://arxiv.org/abs/2606.03078v1
• Date: Tue, 02 Jun 2026 03:09:15 GMT
• Title: G^2C-MT: Graph-Guided Context Selection for Document-Level Machine Translation
• Abstract summary: Document-level machine translation (DocMT) requires capturing long-range discourse dependencies.<n>G2C-MT views DocMT context selection as a structured path discovery problem on a lightweight discourse graph.<n>This framework naturally supports multi-path context sampling, which can improve robustness by aggregating diverse translation candidates for discourse-ambiguous inputs.
• Score: 7.730467866001466
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Effective document-level machine translation (DocMT) requires capturing long-range discourse dependencies. Recent work has explored retrieval-based and discourse-aware context selection. However, these approaches often lack an explicit mechanism for modeling structured discourse dependencies between distant paragraphs in a document. In this paper, we propose G^2C-MT (Graph-Guided Context for Machine Translation), which views DocMT context selection as a structured path discovery problem on a lightweight discourse graph, rather than retrieving unstructured context sets or relying on expensive LLM-based discourse modeling. In detail, we represent each paragraph as a node and model the relationship between each pair of nodes, considering their semantic similarity, adjacency, and keyword overlap. Furthermore, we propose a depth-biased random walk over the graph to sample a backward context path for each target paragraph. The context path will be used to prompt a large language model (LLM) for translation. This framework naturally supports multi-path context sampling, which can improve robustness by aggregating diverse translation candidates for discourse-ambiguous inputs. Experiments conducted across various domains show that G^2C-MT outperforms strong baselines on multiple LLMs, including DeepSeek-V3, Gemini-2.5-Flash-lite, and the Qwen-2.5/3 series.

Related papers

• Chain of Evidence: Pixel-Level Visual Attribution for Iterative Retrieval-Augmented Generation [19.889854990300595]
  Iterative Retrieval-Augmented Generation (iRAG) has emerged as a powerful paradigm for answering complex multi-hop questions.<n>We present textbfChain of Evidence (CoE), a retriever-agnostic visual attribution framework.
  arXiv  Detail & Related papers  (2026-05-02T06:40:42Z)
• $G^2$-Reader: Dual Evolving Graphs for Multimodal Document QA [53.491241153213565]
  $G2$-Reader is a dual-graph system for multimodal question answering.<n>$G2$-Reader with Qwen3-VL-32B-Instruct reaches 66.21% average accuracy, outperforming strong baselines and a standalone GPT-5 (53.08%)<n>On VisDoMBench across five multimodal domains, $G2$-Reader with Qwen3-VL-32B-Instruct reaches 66.21% average accuracy, outperforming strong baselines and a standalone GPT-5 (53.08%)
  arXiv  Detail & Related papers  (2026-01-29T17:52:54Z)
• Discourse Graph Guided Document Translation with Large Language Models [18.88786853549414]
  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.
  arXiv  Detail & Related papers  (2025-11-10T15:48:01Z)
• 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)
• Beyond Chunking: Discourse-Aware Hierarchical Retrieval for Long Document Question Answering [51.7493726399073]
  We present a discourse-aware hierarchical framework to enhance long document question answering.<n>The framework involves three key innovations: specialized discourse parsing for lengthy documents, LLM-based enhancement of discourse relation nodes, and structure-guided hierarchical retrieval.
  arXiv  Detail & Related papers  (2025-05-26T14:45:12Z)
• M-DocSum: Do LVLMs Genuinely Comprehend Interleaved Image-Text in Document Summarization? [49.53982792497275]
  We investigate whether Large Vision-Language Models (LVLMs) genuinely comprehend interleaved image-text in the document.<n>Existing document understanding benchmarks often assess LVLMs using question-answer formats.<n>We introduce a novel and challenging Multimodal Document Summarization Benchmark (M-DocSum-Bench)<n>M-DocSum-Bench comprises 500 high-quality arXiv papers, along with interleaved multimodal summaries aligned with human preferences.
  arXiv  Detail & Related papers  (2025-03-27T07:28:32Z)
• A Case Study on Context-Aware Neural Machine Translation with Multi-Task Learning [49.62044186504516]
  In document-level neural machine translation (DocNMT), multi-encoder approaches are common in encoding context and source sentences. Recent studies have shown that the context encoder generates noise and makes the model robust to the choice of context. This paper further investigates this observation by explicitly modelling context encoding through multi-task learning (MTL) to make the model sensitive to the choice of context.
  arXiv  Detail & Related papers  (2024-07-03T12:50:49Z)
• MGDoc: Pre-training with Multi-granular Hierarchy for Document Image Understanding [53.03978356918377]
  spatial hierarchical relationships between content at different levels of granularity are crucial for document image understanding tasks. Existing methods learn features from either word-level or region-level but fail to consider both simultaneously. We propose MGDoc, a new multi-modal multi-granular pre-training framework that encodes page-level, region-level, and word-level information at the same time.
  arXiv  Detail & Related papers  (2022-11-27T22:47:37Z)
• UnifieR: A Unified Retriever for Large-Scale Retrieval [84.61239936314597]
  Large-scale retrieval is to recall relevant documents from a huge collection given a query. Recent retrieval methods based on pre-trained language models (PLM) can be coarsely categorized into either dense-vector or lexicon-based paradigms. We propose a new learning framework, UnifieR which unifies dense-vector and lexicon-based retrieval in one model with a dual-representing capability.
  arXiv  Detail & Related papers  (2022-05-23T11:01:59Z)
• SMDT: Selective Memory-Augmented Neural Document Translation [53.4627288890316]
  We propose a Selective Memory-augmented Neural Document Translation model to deal with documents containing large hypothesis space of context. We retrieve similar bilingual sentence pairs from the training corpus to augment global context. We extend the two-stream attention model with selective mechanism to capture local context and diverse global contexts.
  arXiv  Detail & Related papers  (2022-01-05T14:23:30Z)

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.