# An Effective Approach to Unsupervised Machine Translation

## TL;DR

This paper presents an improved unsupervised machine translation approach that leverages subword information, a theoretically grounded tuning method, and joint refinement, achieving state-of-the-art results in English-German translation.

## Contribution

It introduces novel techniques for unsupervised SMT and NMT, including subword exploitation, a new tuning method, and joint refinement, significantly enhancing translation quality.

## Key findings

- Achieved 22.5 BLEU in English-German translation, surpassing previous unsupervised methods.
- Improved translation performance by 5.5 BLEU points over prior unsupervised systems.
- Outperformed the 2014 supervised shared task winner in BLEU score.

## Abstract

While machine translation has traditionally relied on large amounts of parallel corpora, a recent research line has managed to train both Neural Machine Translation (NMT) and Statistical Machine Translation (SMT) systems using monolingual corpora only. In this paper, we identify and address several deficiencies of existing unsupervised SMT approaches by exploiting subword information, developing a theoretically well founded unsupervised tuning method, and incorporating a joint refinement procedure. Moreover, we use our improved SMT system to initialize a dual NMT model, which is further fine-tuned through on-the-fly back-translation. Together, we obtain large improvements over the previous state-of-the-art in unsupervised machine translation. For instance, we get 22.5 BLEU points in English-to-German WMT 2014, 5.5 points more than the previous best unsupervised system, and 0.5 points more than the (supervised) shared task winner back in 2014.