Congolese Swahili Machine Translation for Humanitarian Response

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Africa NLP workshop organized within the 16th Conference of the European Chapter of the Association for Computational Linguistics (EACL2021)

Alp Öktem, Eric DeLuca, Rodrigue Bashizi, Eric Paquin, Grace Tang
Congolese Swahili Machine Translation for Humanitarian Response
In: Africa NLP workshop organized within the 16th Conference of the European Chapter of the Association for Computational Linguistics (EACL2021)
2021 April 19; Virtual. 

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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Data and models

Poster

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