SIGTYP 2020 Shared Task: Prediction of Typological Features

Johannes Bjerva, Elizabeth Salesky, Sabrina J. Mielke, Aditi Chaudhary, Celano Giuseppe, Edoardo Maria Ponti, Ekaterina Vylomova, Ryan Cotterell, Isabelle Augenstein


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.
Anthology ID:
2020.sigtyp-1.1
Volume:
Proceedings of the Second Workshop on Computational Research in Linguistic Typology
Month:
November
Year:
2020
Address:
Online
Venues:
EMNLP | SIGTYP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
1–11
Language:
URL:
https://www.aclweb.org/anthology/2020.sigtyp-1.1
DOI:
10.18653/v1/2020.sigtyp-1.1
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PDF:
http://aclanthology.lst.uni-saarland.de/2020.sigtyp-1.1.pdf