# Document Graph for Neural Machine Translation

arXiv: https://arxiv.org/abs/2012.03477 · HTML: https://mlsanity.com/paper/2012.03477/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Dec 2020.

## Built on by (most cited first, 12 of 12)

- [Retrieval-Augmented Generation with Graphs (GraphRAG)](https://mlsanity.com/paper/2501.00309/) (2024)
- [Efficiently Exploring Large Language Models for Document-Level Machine Translation with In-context Learning](https://mlsanity.com/paper/2406.07081/) (2024)
- [BIC: Twitter Bot Detection with Text-Graph Interaction and Semantic Consistency](https://mlsanity.com/paper/2208.08320/) (2022)
- [Enhancing Document-level Translation of Large Language Model via Translation Mixed-instructions](https://mlsanity.com/paper/2401.08088/) (2024)
- [P-Transformer: Towards Better Document-to-Document Neural Machine Translation](https://mlsanity.com/paper/2212.05830/) (2022)
- [Neural Machine Translation with Dynamic Graph Convolutional Decoder](https://mlsanity.com/paper/2305.17698/) (2023)
- [Exploring Paracrawl for Document-level Neural Machine Translation](https://mlsanity.com/paper/2304.10216/) (2023)
- [Context-Aware Machine Translation with Source Coreference Explanation](https://mlsanity.com/paper/2404.19505/) (2024)
- [Discourse Graph Guided Document Translation with Large Language Models](https://mlsanity.com/paper/2511.07230/) (2025)
- [Graph Neural Networks for Natural Language Processing: A Survey](https://mlsanity.com/paper/2106.06090/) (2021)
- [Detecting Political Opinions in Tweets through Bipartite Graph Analysis: A Skip Aggregation Graph Convolution Approach](https://mlsanity.com/paper/2304.11367/) (2023)
- [Promoting Target Data in Context-aware Neural Machine Translation](https://mlsanity.com/paper/2402.06342/) (2024)

## Abstract (arXiv)

Previous works have shown that contextual information can improve the performance of neural machine translation (NMT). However, most existing document-level NMT methods only consider a few number of previous sentences. How to make use of the whole document as global contexts is still a challenge. To address this issue, we hypothesize that a document can be represented as a graph that connects relevant contexts regardless of their distances. We employ several types of relations, including adjacency, syntactic dependency, lexical consistency, and coreference, to construct the document graph. Then, we incorporate both source and target graphs into the conventional Transformer architecture with graph convolutional networks. 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. Extensive analysis verifies that the document graph is beneficial for capturing discourse phenomena.