# From Bilingual to Multilingual Neural Machine Translation by Incremental Training

## TL;DR

This paper introduces an incremental training method for multilingual neural machine translation that scales to more languages without retraining the entire system, using joint training and language-independent modules.

## Contribution

It proposes a novel training schedule enabling scalable multilingual NMT with zero-shot translation, avoiding retraining of previous components.

## Key findings

- Achieves results close to state-of-the-art in WMT tasks
- Enables adding new languages without retraining existing models
- Supports zero-shot translation capabilities

## Abstract

Multilingual Neural Machine Translation approaches are based on the use of task-specific models and the addition of one more language can only be done by retraining the whole system. In this work, we propose a new training schedule that allows the system to scale to more languages without modification of the previous components based on joint training and language-independent encoder/decoder modules allowing for zero-shot translation. This work in progress shows close results to the state-of-the-art in the WMT task.