The SIGMORPHON 2022 shared task on morpheme segmentation challenged systems to
decompose a word into a sequence of morphemes and covered most types of morphology …

We describe the design and findings of the SIGMORPHON 2020 shared task on multilingual
grapheme-to-phoneme conversion. Participants were asked to submit systems which take in …

Many common character-level, string-to-string transduction tasks, eg graphemeto-phoneme
conversion and morphological inflection, consist almost exclusively of monotonic …

Development sets are impractical to obtain for real low-resource languages, since using all
available data for training is often more effective. However, development sets are widely …

An intermediate step in the linguistic analysis of an under-documented language is to find
and organize inflected forms that are attested in natural speech. From this data, linguists …

Deep learning sequence models have been successfully applied to the task of
morphological inflection. The results of the SIGMORPHON shared tasks in the past several …

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

We propose the task of unsupervised morphological paradigm completion. Given only raw
text and a lemma list, the task consists of generating the morphological paradigms, ie, all …

Canonical morphological segmentation consists of dividing words into their standardized
morphemes. Here, we are interested in approaches for the task when training data is limited …

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