# Neural Phrase-to-Phrase Machine Translation

arXiv: https://arxiv.org/abs/1811.02172 · HTML: https://mlsanity.com/paper/1811.02172/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Nov 2018.

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

- [Towards Making the Most of ChatGPT for Machine Translation](https://mlsanity.com/paper/2303.13780/) (2023)
- [Attention, please! A survey of Neural Attention Models in Deep Learning](https://mlsanity.com/paper/2103.16775/) (2021)
- [PAEG: Phrase-level Adversarial Example Generation for Neural Machine Translation](https://mlsanity.com/paper/2201.02009/) (2022)
- [Alleviate Exposure Bias in Sequence Prediction  with Recurrent Neural Networks](https://mlsanity.com/paper/2103.11603/) (2021)

## Abstract (arXiv)

In this paper, we propose Neural Phrase-to-Phrase Machine Translation (NP2MT). Our model uses a phrase attention mechanism to discover relevant input (source) segments that are used by a decoder to generate output (target) phrases. We also design an efficient dynamic programming algorithm to decode segments that allows the model to be trained faster than the existing neural phrase-based machine translation method by Huang et al. (2018). Furthermore, our method can naturally integrate with external phrase dictionaries during decoding. Empirical experiments show that our method achieves comparable performance with the state-of-the art methods on benchmark datasets. However, when the training and testing data are from different distributions or domains, our method performs better.