This paper describes efforts to annotate a dataset of verbs in the Iroquoian language Kanien’kéha (a.k.a. Mohawk) using the UniMorph schema (Batsuren et al. 2022a). It is based on the output of a symbolic model - a hand-built verb conjugator. Morphological constituents of each verb are automatically annotated with UniMorph tags. Overall the process was smooth but some central features of the language did not fall neatly into the schema which resulted in a large number of custom tags and a somewhat ad hoc mapping process. We think the same difficulties are likely to arise for other Iroquoian languages and perhaps other North American language families. This paper describes our decision making process with respect to Kanien’kéha and reports preliminary results of morphological induction experiments using the dataset.

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.