Congolese Swahili Machine Translation for Humanitarian Response
This work addresses translation needs for humanitarian response in the Democratic Republic of Congo, though it is incremental as it applies existing low-resource methods to a new language pair.
The paper tackled the problem of low-resource machine translation for Congolese Swahili to French to aid humanitarian efforts, achieving improvements of up to 2.4 and 3.5 BLEU points and human evaluation scores of 6.3 out of 10 with 75% message conveyance.
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.