The CoNLL-SIGMORPHON 2017 shared task worked to promote the development of robust systems that can learn to perform cross-linguistically reliable morphological inflection and morpholog-
ical paradigm cell filling using varying amounts of training data. We note that this is also the first CoNLL-hosted shared task to focus on morphology. The task itself featured training and
development data from 52 languages representing a range of language families. Many of the languages included were extremely low-resource, e.g., Quechua, Navajo, and Haida. The chosen
languages also encompassed diverse morphological properties and inflection processes. In one task submitted systems were asked to predict a specific inflected form of a given lemma. In
another task systems were given a lemma and some of its specific inflected forms, and asked to complete the inflectional paradigm by predicting all of the remaining inflected forms. Both sub-tasks included high, medium, and low-resource conditions.
Forum
Year
2017
Link to publication
