SIGTYP 2020 Shared Task: Prediction of Typological Features

Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review

Standard

SIGTYP 2020 Shared Task: Prediction of Typological Features. / Bjerva, Johannes; Salesky, Elizabeth; Mielke, Sabrina J.; Chaudhary, Aditi; Giuseppe, Celano; Ponti, Edoardo Maria; Vylomova, Ekaterina; Cotterell, Ryan; Augenstein, Isabelle.

Proceedings of the Second Workshop on Computational Research in Linguistic Typology. Association for Computational Linguistics, 2020. p. 1-11.

Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review

Harvard

Bjerva, J, Salesky, E, Mielke, SJ, Chaudhary, A, Giuseppe, C, Ponti, EM, Vylomova, E, Cotterell, R & Augenstein, I 2020, SIGTYP 2020 Shared Task: Prediction of Typological Features. in Proceedings of the Second Workshop on Computational Research in Linguistic Typology. Association for Computational Linguistics, pp. 1-11, The 2020 Conference on Empirical Methods in Natural Language Processing, 16/11/2020. https://doi.org/10.18653/v1/2020.sigtyp-1.1

APA

Bjerva, J., Salesky, E., Mielke, S. J., Chaudhary, A., Giuseppe, C., Ponti, E. M., Vylomova, E., Cotterell, R., & Augenstein, I. (2020). SIGTYP 2020 Shared Task: Prediction of Typological Features. In Proceedings of the Second Workshop on Computational Research in Linguistic Typology (pp. 1-11). Association for Computational Linguistics. https://doi.org/10.18653/v1/2020.sigtyp-1.1

Vancouver

Bjerva J, Salesky E, Mielke SJ, Chaudhary A, Giuseppe C, Ponti EM et al. SIGTYP 2020 Shared Task: Prediction of Typological Features. In Proceedings of the Second Workshop on Computational Research in Linguistic Typology. Association for Computational Linguistics. 2020. p. 1-11 https://doi.org/10.18653/v1/2020.sigtyp-1.1

Author

Bjerva, Johannes ; Salesky, Elizabeth ; Mielke, Sabrina J. ; Chaudhary, Aditi ; Giuseppe, Celano ; Ponti, Edoardo Maria ; Vylomova, Ekaterina ; Cotterell, Ryan ; Augenstein, Isabelle. / SIGTYP 2020 Shared Task: Prediction of Typological Features. Proceedings of the Second Workshop on Computational Research in Linguistic Typology. Association for Computational Linguistics, 2020. pp. 1-11

Bibtex

@inproceedings{b0479f74c3fe4b2e9d8106d52497078e,
title = "SIGTYP 2020 Shared Task: Prediction of Typological Features",
abstract = "Typological knowledge bases (KBs) such as WALS (Dryer and Haspelmath, 2013) contain information about linguistic properties of the world{\textquoteright}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.",
author = "Johannes Bjerva and Elizabeth Salesky and Mielke, {Sabrina J.} and Aditi Chaudhary and Celano Giuseppe and Ponti, {Edoardo Maria} and Ekaterina Vylomova and Ryan Cotterell and Isabelle Augenstein",
year = "2020",
doi = "10.18653/v1/2020.sigtyp-1.1",
language = "English",
pages = "1--11",
booktitle = "Proceedings of the Second Workshop on Computational Research in Linguistic Typology",
publisher = "Association for Computational Linguistics",
note = "The 2020 Conference on Empirical Methods in Natural Language Processing, EMNLP 2020 ; Conference date: 16-11-2020 Through 20-11-2020",
url = "http://2020.emnlp.org",

}

RIS

TY - GEN

T1 - SIGTYP 2020 Shared Task: Prediction of Typological Features

AU - Bjerva, Johannes

AU - Salesky, Elizabeth

AU - Mielke, Sabrina J.

AU - Chaudhary, Aditi

AU - Giuseppe, Celano

AU - Ponti, Edoardo Maria

AU - Vylomova, Ekaterina

AU - Cotterell, Ryan

AU - Augenstein, Isabelle

PY - 2020

Y1 - 2020

N2 - 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.

AB - 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.

U2 - 10.18653/v1/2020.sigtyp-1.1

DO - 10.18653/v1/2020.sigtyp-1.1

M3 - Article in proceedings

SP - 1

EP - 11

BT - Proceedings of the Second Workshop on Computational Research in Linguistic Typology

PB - Association for Computational Linguistics

T2 - The 2020 Conference on Empirical Methods in Natural Language Processing

Y2 - 16 November 2020 through 20 November 2020

ER -

ID: 254997462