@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://aclanthology.org/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.",
}
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<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.</abstract>
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%0 Conference Proceedings
%T SIGTYP 2020 Shared Task: Prediction of Typological Features
%A Bjerva, Johannes
%A Salesky, Elizabeth
%A Mielke, Sabrina J.
%A Chaudhary, Aditi
%A Celano, Giuseppe G. A.
%A Ponti, Edoardo Maria
%A Vylomova, Ekaterina
%A Cotterell, Ryan
%A Augenstein, Isabelle
%Y Vylomova, Ekaterina
%Y Ponti, Edoardo M.
%Y Grossman, Eitan
%Y McCarthy, Arya D.
%Y Berzak, Yevgeni
%Y Dubossarsky, Haim
%Y Vulić, Ivan
%Y Reichart, Roi
%Y Korhonen, Anna
%Y Cotterell, Ryan
%S Proceedings of the Second Workshop on Computational Research in Linguistic Typology
%D 2020
%8 November
%I Association for Computational Linguistics
%C Online
%F bjerva-etal-2020-sigtyp
%X 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.
%R 10.18653/v1/2020.sigtyp-1.1
%U https://aclanthology.org/2020.sigtyp-1.1
%U https://doi.org/10.18653/v1/2020.sigtyp-1.1
%P 1-11
Markdown (Informal)
[SIGTYP 2020 Shared Task: Prediction of Typological Features](https://aclanthology.org/2020.sigtyp-1.1) (Bjerva et al., SIGTYP 2020)
ACL
- Johannes Bjerva, Elizabeth Salesky, Sabrina J. Mielke, Aditi Chaudhary, Giuseppe G. A. Celano, Edoardo Maria Ponti, Ekaterina Vylomova, Ryan Cotterell, and Isabelle Augenstein. 2020. SIGTYP 2020 Shared Task: Prediction of Typological Features. In Proceedings of the Second Workshop on Computational Research in Linguistic Typology, pages 1–11, Online. Association for Computational Linguistics.