Predicting Typological Features in WALS using Language Embeddings and Conditional Probabilities: \'UFAL Submission to the SIGTYP 2020 Shared Task

• URL: http://arxiv.org/abs/2010.03920v1
• Date: Thu, 8 Oct 2020 12:05:48 GMT
• Title: Predicting Typological Features in WALS using Language Embeddings and Conditional Probabilities: \'UFAL Submission to the SIGTYP 2020 Shared Task
• Authors: Martin Vastl, Daniel Zeman, Rudolf Rosa
• Abstract summary: We submit a constrained system, predicting typological features only based on the WALS database. We reach the accuracy of 70.7% on the test data and rank first in the shared task.
• Score: 1.4848029858256582
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: 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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