SIGTYP 2020 Shared Task: Prediction of Typological Features

• URL: http://arxiv.org/abs/2010.08246v2
• Date: Mon, 26 Oct 2020 07:29:45 GMT
• Title: SIGTYP 2020 Shared Task: Prediction of Typological Features
• Authors: Johannes Bjerva and Elizabeth Salesky and Sabrina J. Mielke and Aditi Chaudhary and Giuseppe G. A. Celano and Edoardo M. Ponti and Ekaterina Vylomova and Ryan Cotterell and Isabelle Augenstein
• Abstract summary: A major drawback hampering broader adoption of typological KBs is that they are sparsely populated. As typological features often correlate with one another, it is possible to predict them and thus automatically populate typological KBs. Overall, the task attracted 8 submissions from 5 teams, out of which the most successful methods make use of such feature correlations.
• Score: 78.95376120154083
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Typological knowledge bases (KBs) such as WALS (Dryer and Haspelmath, 2013) contain information about linguistic properties of the world's languages. They have been shown to be useful for downstream applications, including cross-lingual transfer learning and linguistic probing. A major drawback hampering broader adoption of typological KBs is that they are sparsely populated, in the sense that most languages only have annotations for some features, and skewed, in that few features have wide coverage. As typological features often correlate with one another, it is possible to predict them and thus automatically populate typological KBs, which is also the focus of this shared task. Overall, the task attracted 8 submissions from 5 teams, out of which the most successful methods make use of such feature correlations. However, our error analysis reveals that even the strongest submitted systems struggle with predicting feature values for languages where few features are known.

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