Translators at the Canadian Parliament currently have access to a neural machine translation system as an optional tool integrated into their translation environment, whose output they can use for postediting (rather than translating from scratch); this provides a valuable opportunity to study the dynamics of MT adoption in professional settings. We report on a user study that investigates how and why translators choose to interact with this tool. Using a mixed-methods approach, we examined both human and technical factors that influence the adoption or non-adoption of the system. Drawing on our findings, we advocate for a user-centred approach to MT integration within professional translation workflows.

The Parliament of Canada’s translation workflow includes access to a specialized neural machine translation (NMT) system. This study analyzes post-editing (PE) activity to identify the types of edits translators make when interacting with the NMT system, as well as the frequency, nature, and severity of errors encountered. We compare translations produced with and without the use of this NMT system to evaluate potential differences in edit patterns. To complement this analysis, we draw on insights from a user study. Our findings explore how translators’ perceptions align with observed PE patterns and how their feedback can inform strategies to better understand, and possibly mitigate, some of the errors observed.

We develop an interactive web-based user interface for performing textspeech alignment and creating digital interactive “read-along audio books that highlight words as they are spoken and allow users to replay individual words when clicked. We build on an existing Python library for zero-shot multilingual textspeech alignment (Littell et al., 2022), extend it by exposing its functionality through a RESTful API, and rewrite the underlying speech recognition engine to run in the browser. The ReadAlong Studio Web App is open-source, user-friendly, prioritizes privacy and data sovereignty, allows for a variety of standard export formats, and is designed to work for the majority of the world’s languages.

Plains Cree (nêhiyawêwin) is an Indigenous language that is spoken in Canada and the USA. It is the most widely spoken dialect of Cree and a morphologically complex language that is polysynthetic, highly inflective, and agglutinative. It is an extremely low resource language, with no existing corpus that is both available and prepared for supporting the development of language technologies. To support nêhiyawêwin revitalization and preservation, we developed a corpus covering diverse genres, time periods, and texts for a variety of intended audiences. The data has been verified and cleaned; it is ready for use in developing language technologies for nêhiyawêwin. The corpus includes the corresponding English phrases or audio files where available. We demonstrate the utility of the corpus through its community use and its use to build language technologies that can provide the types of support that community members have expressed are desirable. The corpus is available for public use.

This paper surveys the first, three-year phase of a project at the National Research Council of Canada that is developing software to assist Indigenous communities in Canada in preserving their languages and extending their use. The project aimed to work within the empowerment paradigm, where collaboration with communities and fulfillment of their goals is central. Since many of the technologies we developed were in response to community needs, the project ended up as a collection of diverse subprojects, including the creation of a sophisticated framework for building verb conjugators for highly inflectional polysynthetic languages (such as Kanyen’kéha, in the Iroquoian language family), release of what is probably the largest available corpus of sentences in a polysynthetic language (Inuktut) aligned with English sentences and experiments with machine translation (MT) systems trained on this corpus, free online services based on automatic speech recognition (ASR) for easing the transcription bottleneck for recordings of speech in Indigenous languages (and other languages), software for implementing text prediction and read-along audiobooks for Indigenous languages, and several other subprojects.