  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.
