Congolese Swahili Machine Translation for Humanitarian Response

• URL: http://arxiv.org/abs/2103.10734v1
• Date: Fri, 19 Mar 2021 11:15:48 GMT
• Title: Congolese Swahili Machine Translation for Humanitarian Response
• Authors: Alp \"Oktem, Eric DeLuca, Rodrigue Bashizi, Eric Paquin, Grace Tang
• Abstract summary: We describe our efforts to make a bidirectional Congolese Swahili to French neural machine translation system. For training, we created a 25,302-sentence general domain parallel corpus. We recorded improvements of up to 2.4 and 3.5 BLEU points in the SWC-FRA and FRA-SWC directions.
• Score: 0.05526111147542002
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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