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Typological knowledge bases (KBs) such as WALS (Dryer and Haspelmath, 2013)contain information about linguistic properties of the world's languages. Theyhave been shown to be useful for downstream applications, includingcross-lingual transfer learning and linguistic probing. A major drawbackhampering broader adoption of typological KBs is that they are sparselypopulated, in the sense that most languages only have annotations for somefeatures, and skewed, in that few features have wide coverage. As typologicalfeatures often correlate with one another, it is possible to predict them andthus automatically populate typological KBs, which is also the focus of thisshared task. Overall, the task attracted 8 submissions from 5 teams, out ofwhich the most successful methods make use of such feature correlations.However, our error analysis reveals that even the strongest submitted systemsstruggle with predicting feature values for languages where few features areknown.

SIGTYP 2020 Shared Task: Prediction of Typological Features