Proceedings of the Second Workshop on Computational Research in Linguistic Typology

Ekaterina Vylomova, Edoardo M. Ponti, Eitan Grossman, Arya D. McCarthy, Yevgeni Berzak, Haim Dubossarsky, Ivan Vulić, Roi Reichart, Anna Korhonen, Ryan Cotterell (Editors)


Anthology ID:
2020.sigtyp-1
Month:
November
Year:
2020
Address:
Online
Venues:
EMNLP | SIGTYP
SIG:
SIGTYP
Publisher:
Association for Computational Linguistics
URL:
https://aclanthology.org/2020.sigtyp-1
DOI:
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Proceedings of the Second Workshop on Computational Research in Linguistic Typology
Ekaterina Vylomova | Edoardo M. Ponti | Eitan Grossman | Arya D. McCarthy | Yevgeni Berzak | Haim Dubossarsky | Ivan Vulić | Roi Reichart | Anna Korhonen | Ryan Cotterell

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

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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Predicting Typological Features in WALS using Language Embeddings and Conditional Probabilities : FAL Submission to the SIGTYP 2020 Shared TaskWALS using Language Embeddings and Conditional Probabilities: ÚFAL Submission to the SIGTYP 2020 Shared Task
Martin Vastl | Daniel Zeman | Rudolf Rosa

We present our submission to the SIGTYP 2020 Shared Task on the prediction of typological features. We submit a constrained system, predicting typological features only based on the WALS database. We investigate two approaches. The simpler of the two is a system based on estimating correlation of feature values within languages by computing conditional probabilities and mutual information. The second approach is to train a neural predictor operating on precomputed language embeddings based on WALS features. Our submitted system combines the two approaches based on their self-estimated confidence scores. We reach the accuracy of 70.7 % on the test data and rank first in the shared task.

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NUIG : Multitasking Self-attention based approach to SigTyp 2020 Shared TaskNUIG: Multitasking Self-attention based approach to SigTyp 2020 Shared Task
Chinmay Choudhary

The paper describes the Multitasking Self-attention based approach to constrained sub-task within Sigtyp 2020 Shared task. Our model is simple neural network based architecture inspired by Transformers (CITATION) model. The model uses Multitasking to compute values of all WALS features for a given input language simultaneously. Results show that our approach performs at par with the baseline approaches, even though our proposed approach requires only phylogenetic and geographical attributes namely Longitude, Latitude, Genus-index, Family-index and Country-index and do not use any of the known WALS features of the respective input language, to compute its missing WALS features.Multitasking Self-attention based approach to constrained sub-task within Sigtyp 2020 Shared task. Our model is simple neural network based architecture inspired by Transformers (CITATION) model. The model uses Multitasking to compute values of all WALS features for a given input language simultaneously.\n\nResults show that our approach performs at par with the baseline approaches, even though our proposed approach requires only phylogenetic and geographical attributes namely Longitude, Latitude, Genus-index, Family-index and Country-index and do not use any of the known WALS features of the respective input language, to compute its missing WALS features.