The Universal Morphology (UniMorph) project is a collaborative effort providing broad-
coverage instantiated normalized morphological paradigms for hundreds of diverse world …

We present findings from the creation of a massively parallel corpus in over 1600
languages, the Johns Hopkins University Bible Corpus (JHUBC). The corpus consists of …

What can pre-trained multilingual sequence-to-sequence models like mBART contribute to
translating low-resource languages? We conduct a thorough empirical experiment in 10 …

Automatic morphological processing can aid downstream natural language processing
applications, especially for low-resource languages, and assist language documentation …

In this paper, we describe the findings of the SIGMORPHON 2020 shared task on
unsupervised morphological paradigm completion (SIGMORPHON 2020 Task 2), a novel …

Large multilingual models have inspired a new class of word alignment methods, which
work well for the model's pretraining languages. However, the languages most in need of …

Grapheme-to-phoneme conversion is an important component in many speech
technologies, but until recently there were no multilingual benchmarks for this task. The third …

Due to the lack of data resources, rule-based or transfer learning is mainly used in the
morphological tagging of low-resource languages. However, these methods require expert …

This paper describes an ongoing effort to create, from the original hand-written text, a
machine-readable, linguistically-annotated, and easily-searchable corpus of the Nahuatl …

We describe a suite of finite-state language technologies for Maya, a Mayan language
spoken in Mexico. At the core is a computational model of Maya morphology and phonology …