# Building a Neural Machine Translation System Using Only Synthetic Parallel Data

arXiv: https://arxiv.org/abs/1704.00253 · HTML: https://mlsanity.com/paper/1704.00253/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Apr 2017.

## Built on by (most cited first, 1 of 1)

- [m4Adapter: Multilingual Multi-Domain Adaptation for Machine Translation with a Meta-Adapter](https://mlsanity.com/paper/2210.11912/) (2022)

## Abstract (arXiv)

Recent works have shown that synthetic parallel data automatically generated by translation models can be effective for various neural machine translation (NMT) issues. In this study, we build NMT systems using only synthetic parallel data. As an efficient alternative to real parallel data, we also present a new type of synthetic parallel corpus. The proposed pseudo parallel data are distinct from previous works in that ground truth and synthetic examples are mixed on both sides of sentence pairs. Experiments on Czech-German and French-German translations demonstrate the efficacy of the proposed pseudo parallel corpus, which shows not only enhanced results for bidirectional translation tasks but also substantial improvement with the aid of a ground truth real parallel corpus.