Evaluating nêhiyawêtân: A computer assisted language learning (CALL) application for Plains Cree

Bontogon, Megan

doi:10.7939/r3vd6p81c

Cited by 1 publication

(2 citation statements)

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“…Successful examples of participatory research in NLP exist, such as the Masakhane project for African languages [29]. Some computational linguistics tools for learning have also been evaluated in part with native speakers, such as nêhiyawêtân [4] for which feedback was collected from usage of the tool and noted its usefulness for users. Practically speaking, and for the rst steps of our project, our approach has been dened based on the following principles.…”

Section: Community-driven Approachmentioning

confidence: 99%

“…One of more than 70 Indigenous languages in Canada, Innu-Aimun was spoken in 2021 by 11,605 locutors (including Naskapi), making it the 4th most spoken in the country. This number has been decreasing in recent years 4 . And yet, there has been for several decades a growing body of Innu literature written both in Innu-Aimun and French, a movement that takes its origins from oral stories [34].…”

Section: Introductionmentioning

confidence: 99%

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Lost in Innu-Aimun Translation - Re-dening Neural Machine Translation for Indigenous Interpreters and Translators Needs

Cadotte,

Thernish,

Sadat

2023

Proceedings of the International Conference on Human-Informed Translation and Interpreting Technology 2023

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Innu-Aimun, one of the most spoken Indigenous languages in Canada, faces signicant transmission challenges. Although there is a notable body of Innu-Aimun literature, there is generally not enough documentation written in Innu-Aimun for daily use, just as there are not enough translators and interpreters for the language. We present here collaborative work between Innu-Aimun translators and researchers in computational linguistics to develop translation assistance tools, with the aim of helping language revitalization and preservation. We detail our common position on how should technological assistance tools be developed for Innu-Aimun, which emphasizes the importance of involving Innu translators throughout the entire process and making sure to address language-specic needs. This position is elaborated from joining our respective perspectives (researchers and Innu community member) and expertise (computational linguistics and Innu-Aimun translation). In this spirit, we present preliminary results for the rst ongoing steps towards building a rst Innu-Aimun -French Neural Machine Translation model. We focus on our participatory process to create aligned parallel corpora and present rst results and analyses.

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Section: Community-driven Approachmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%