# Document-level Neural Machine Translation with Document Embeddings

arXiv: https://arxiv.org/abs/2009.08775 · HTML: https://mlsanity.com/paper/2009.08775/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Sep 2020.

## Built on by (most cited first, 2 of 2)

- [Seeking Common but Distinguishing Difference, A Joint Aspect-based Sentiment Analysis Model](https://mlsanity.com/paper/2111.09634/) (2021)
- [Improving Long Context Document-Level Machine Translation](https://mlsanity.com/paper/2306.05183/) (2023)

## Abstract (arXiv)

Standard neural machine translation (NMT) is on the assumption of document-level context independent. Most existing document-level NMT methods are satisfied with a smattering sense of brief document-level information, while this work focuses on exploiting detailed document-level context in terms of multiple forms of document embeddings, which is capable of sufficiently modeling deeper and richer document-level context. The proposed document-aware NMT is implemented to enhance the Transformer baseline by introducing both global and local document-level clues on the source end. Experiments show that the proposed method significantly improves the translation performance over strong baselines and other related studies.