Learning to translate by learning to communicate

TL;DR

This paper introduces a novel approach combining emergent communication with a pre-trained multilingual model to enhance unsupervised neural machine translation, especially benefiting low-resource languages through vision-grounded tasks.

Contribution

It proposes a new EC fine-tuning method that aligns multiple languages in a shared task space, improving translation quality over traditional backtranslation methods.

Findings

EC fine-tuning outperforms backtranslation baseline in four languages

Method improves translation for low-resource language Nepali

Multilingual shared task space enhances language alignment

Abstract

We formulate and test a technique to use Emergent Communication (EC) with a pre-trained multilingual model to improve on modern Unsupervised NMT systems, especially for low-resource languages. It has been argued that the current dominant paradigm in NLP of pre-training on text-only corpora will not yield robust natural language understanding systems, and the need for grounded, goal-oriented, and interactive language learning has been high lighted. In our approach, we embed a multilingual model (mBART, Liu et al., 2020) into an EC image-reference game, in which the model is incentivized to use multilingual generations to accomplish a vision-grounded task. The hypothesis is that this will align multiple languages to a shared task space. We present two variants of EC Fine-Tuning (Steinert-Threlkeld et al., 2022), one of which outperforms a backtranslation-only baseline in all four languages investigated, including the low-resource language Nepali.