# Agreement-based Joint Training for Bidirectional Attention-based Neural Machine Translation

arXiv: https://arxiv.org/abs/1512.04650 · HTML: https://mlsanity.com/paper/1512.04650/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Dec 2015.

## Built on by (most cited first, 4 of 4)

- [Duplex Sequence-to-Sequence Learning for Reversible Machine Translation](https://mlsanity.com/paper/2105.03458/) (2021)
- [Modeling Coverage for Non-Autoregressive Neural Machine Translation](https://mlsanity.com/paper/2104.11897/) (2021)
- [Word Alignment in the Era of Deep Learning: A Tutorial](https://mlsanity.com/paper/2212.00138/) (2022)
- [Revamping Multilingual Agreement Bidirectionally via Switched Back-translation for Multilingual Neural Machine Translation](https://mlsanity.com/paper/2209.13940/) (2022)

## Abstract (arXiv)

The attentional mechanism has proven to be effective in improving end-to-end neural machine translation. However, due to the intricate structural divergence between natural languages, unidirectional attention-based models might only capture partial aspects of attentional regularities. We propose agreement-based joint training for bidirectional attention-based end-to-end neural machine translation. Instead of training source-to-target and target-to-source translation models independently,our approach encourages the two complementary models to agree on word alignment matrices on the same training data. Experiments on Chinese-English and English-French translation tasks show that agreement-based joint training significantly improves both alignment and translation quality over independent training.