# English-to-Chinese Transliteration with Phonetic Back-transliteration

arXiv: https://arxiv.org/abs/2112.10321 · HTML: https://mlsanity.com/paper/2112.10321/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 23 references that are in the map.
- First posted on arXiv 20 Dec 2021.

## Builds on (most cited first, 5 of 5)

- [Neural Machine Translation by Jointly Learning to Align and Translate](https://mlsanity.com/paper/1409.0473/) (2014)
- [Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling](https://mlsanity.com/paper/1412.3555/) (2014)
- [Scheduled Sampling for Sequence Prediction with Recurrent Neural Networks](https://mlsanity.com/paper/1506.03099/) (2015)
- [Neural Machine Translation and Sequence-to-sequence Models: A Tutorial](https://mlsanity.com/paper/1703.01619/) (2017)
- [Sequence-to-sequence neural network models for transliteration](https://mlsanity.com/paper/1610.09565/) (2016)

## Abstract (arXiv)

Transliteration is a task of translating named entities from a language to another, based on phonetic similarity. The task has embraced deep learning approaches in recent years, yet, most ignore the phonetic features of the involved languages. In this work, we incorporate phonetic information into neural networks in two ways: we synthesize extra data using forward and back-translation but in a phonetic manner; and we pre-train models on a phonetic task before learning transliteration. Our experiments include three language pairs and six directions, namely English to and from Chinese, Hebrew and Thai. Results indicate that our proposed approach brings benefits to the model and achieves better or similar performance when compared to state of the art.