The annotation of corpora for lower-resource languages can benefit from automatic pre-annotation to increase the throughput of the annotation process in a a context where human resources are scarce. However, this can be hindered by the lack of available pre-annotation tools. In this work, we compare three pre-annotation methods in zero-shot or near-zero-shot contexts for part-of-speech (POS) and dependency annotation of an Alsatian Alemannic dialect. Our study shows that good levels of annotation quality can be achieved, with human annotators adapting their correction effort to the perceived quality of the pre-annotation. The pre-annotation tools also vary in efficiency depending on the task, with better global results for a system trained on closely related languages and dialects.

We present the first corpus of Alsatian Alemannic dialects following Universal Dependencies (UD) guidelines, a project which already covers many of the world’s languages. Standard languages are represented to a greater extent than non-standard varieties in UD, and our corpus contributes to closing the gap in the lack of resources for Alsatian dialects by presenting the first UD treebank for these dialects, which are spoken in Northeastern France. Our corpus is annotated both with part-of-speech tags and dependency information, as well as French glosses and German lemmas, containing in total 975 sentences and 19,286 tokens, spanning over various text genres. In this article, we present our data, details of the annotation process, as well as some specific syntactic phenomena which differentiate and situate Alsatian with regards to both Standard German and some other German non-standard varieties. The addition of this corpus to the UD project allows for a higher visibility of the Alemannic Alsatian dialects in linguistic research, and provides a valuable resource for research in many fields, including NLP, syntax and comparative Germanic linguistics.