  In this paper we describe our efforts to make a bidirectional Congolese
Swahili (SWC) to French (FRA) neural machine translation system with the
motivation of improving humanitarian translation workflows. For training, we
created a 25,302-sentence general domain parallel corpus and combined it with
publicly available data. Experimenting with low-resource methodologies like
cross-dialect transfer and semi-supervised learning, we recorded improvements
of up to 2.4 and 3.5 BLEU points in the SWC-FRA and FRA-SWC directions,
respectively. We performed human evaluations to assess the usability of our
models in a COVID-domain chatbot that operates in the Democratic Republic of
Congo (DRC). Direct assessment in the SWC-FRA direction demonstrated an average
quality ranking of 6.3 out of 10 with 75% of the target strings conveying the
main message of the source text. For the FRA-SWC direction, our preliminary
tests on post-editing assessment showed its potential usefulness for
machine-assisted translation. We make our models, datasets containing up to 1
million sentences, our development pipeline, and a translator web-app available
for public use.
