Synthetic Pre-Training Tasks for Neural Machine Translation

TL;DR

This paper investigates the effectiveness of pre-training neural machine translation models using synthetic data and tasks, aiming to reduce reliance on real-world data and mitigate associated ethical concerns.

Contribution

It introduces novel synthetic pre-training approaches involving obfuscated, concatenated, and purely synthetic data, providing empirical insights into their impact on NMT performance.

Findings

Pre-training benefits persist even with highly obfuscated data.

Synthetic parallel data can effectively replace real corpora.

Insights into factors influencing synthetic data effectiveness for NMT.

Abstract

Pre-training models with large crawled corpora can lead to issues such as toxicity and bias, as well as copyright and privacy concerns. A promising way of alleviating such concerns is to conduct pre-training with synthetic tasks and data, since no real-world information is ingested by the model. Our goal in this paper is to understand the factors that contribute to the effectiveness of pre-training models when using synthetic resources, particularly in the context of neural machine translation. We propose several novel approaches to pre-training translation models that involve different levels of lexical and structural knowledge, including: 1) generating obfuscated data from a large parallel corpus 2) concatenating phrase pairs extracted from a small word-aligned corpus, and 3) generating synthetic parallel data without real human language corpora. Our experiments on multiple language pairs reveal that pre-training benefits can be realized even with high levels of obfuscation or purely synthetic parallel data. We hope the findings from our comprehensive empirical analysis will shed light on understanding what matters for NMT pre-training, as well as pave the way for the development of more efficient and less toxic models.