This work investigates the in-context learning abilities of pretrained large language models (LLMs) when instructed to translate text from a low-resource language into a high-resource language as part of an automated machine translation pipeline. We conduct a set of experiments translating Southern Quechua to Spanish and examine the informativity of various types of information retrieved from a constrained database of digitized pedagogical materials (dictionaries and grammar lessons) and parallel corpora. Using both automatic and human evaluation of model output, we conduct ablation studies that manipulate (1) context type (morpheme translations, grammar descriptions, and corpus examples), (2) retrieval methods (automated vs. manual), and (3) model type. Our results suggest that even relatively small LLMs are capable of utilizing prompt context for zero-shot low-resource translation when provided a minimally sufficient amount of relevant linguistic information. However, the variable effects of prompt type, retrieval method, model type, and language community-specific factors highlight the limitations of using even the best LLMs as translation systems for the majority of the world’s 7,000+ languages and their speakers.

OSU’s inflection system is a transformer whose input is augmented with an analogical exemplar showing how to inflect a different word into the target cell. In addition, alignment-based heuristic features indicate how well the exemplar is likely to match the output. OSU’s scores substantially improve over the baseline transformer for instances where an exemplar is available, though not quite matching the challenge winner. In Part 2, the system shows a tendency to over-apply the majority pattern in English, but not Arabic.

There are many challenges in morphological fieldwork annotation, it heavily relies on segmentation and feature labeling (which have both practical and theoretical drawbacks), it’s time-intensive, and the annotator needs to be linguistically trained and may still annotate things inconsistently. We propose a workflow that relies on unsupervised and active learning grounded in Word-and-Paradigm morphology (WP). Machine learning has the potential to greatly accelerate the annotation process and allow a human annotator to focus on problematic cases, while the WP approach makes for an annotation system that is word-based and relational, removing the need to make decisions about feature labeling and segmentation early in the process and allowing speakers of the language of interest to participate more actively, since linguistic training is not necessary. We present a proof-of-concept for the first step of the workflow, in a realistic fieldwork setting, annotators can process hundreds of forms per hour.

The morphological status of affixes in Chinese has long been a matter of debate. How one might apply the conventional criteria of free/bound and content/function features to distinguish word-forming affixes from bound roots in Chinese is still far from clear. Issues involving polysemy and diachronic dynamics further blur the boundaries. In this paper, we propose three quantitative features in a computational model of affixoid behavior in Mandarin Chinese. The results show that, except for in a very few cases, there are no clear criteria that can be used to identify an affix’s status in an isolating language like Chinese. A diachronic check using contextualized embeddings with the WordNet Sense Inventory also demonstrates the possible role of the polysemy of lexical roots across diachronic settings.