@inproceedings{bjerva-etal-2020-sigtyp,
    title = "{SIGTYP} 2020 Shared Task: Prediction of Typological Features",
    author = "Bjerva, Johannes  and
      Salesky, Elizabeth  and
      Mielke, Sabrina J.  and
      Chaudhary, Aditi  and
      Celano, Giuseppe G. A.  and
      Ponti, Edoardo Maria  and
      Vylomova, Ekaterina  and
      Cotterell, Ryan  and
      Augenstein, Isabelle",
    editor = "Vylomova, Ekaterina  and
      Ponti, Edoardo M.  and
      Grossman, Eitan  and
      McCarthy, Arya D.  and
      Berzak, Yevgeni  and
      Dubossarsky, Haim  and
      Vuli\'c, Ivan  and
      Reichart, Roi  and
      Korhonen, Anna  and
      Cotterell, Ryan",
    booktitle = "Proceedings of the Second Workshop on Computational Research in Linguistic Typology",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/build-pipeline-with-new-library/2020.sigtyp-1.1/",
    doi = "10.18653/v1/2020.sigtyp-1.1",
    pages = "1--11",
    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."
}