  Obtaining high-quality parallel corpora is of paramount importance for
training NMT systems. However, as many language pairs lack adequate
gold-standard training data, a popular approach has been to mine so-called
"pseudo-parallel" sentences from paired documents in two languages. In this
paper, we outline some problems with current methods, propose computationally
economical solutions to those problems, and demonstrate success with novel
methods on the Tatoeba similarity search benchmark and on a downstream task,
namely NMT. We uncover the effect of resource-related factors (i.e. how much
monolingual/bilingual data is available for a given language) on the optimal
choice of bitext mining approach, and echo problems with the oft-used BUCC
dataset that have been observed by others. We make the code and data used for
our experiments publicly available.
