Dynamic-oracle transition-based parsing with calibrated probabilistic output

Yoav Goldberg

Dynamic-oracle transition-based parsing with calibrated probabilistic output

Yoav Goldberg

Department of Computer Science and Artificial Intelligence

Research output: Contribution to conference › Paper › peer-review

5 Scopus citations

Abstract

We adapt the dynamic-oracle training method of Goldberg and Nivre (2012; 2013) to train classifiers that produce probabilistic output. Evaluation of an Arc-Eager parser on 6 languages shows that the AdaGrad-RDA based training procedure results in models that provide the same high level of accuracy as the averaged-perceptron trained models, while being sparser and providing well-calibrated probabilistic output.

Original language	English
Pages	82-90
Number of pages	9
State	Published - 2013
Event	13th International Conference on Parsing Technologies, IWPT 2013 - Nara, Japan Duration: 27 Nov 2013 → 29 Nov 2013

Conference

Conference	13th International Conference on Parsing Technologies, IWPT 2013
Country/Territory	Japan
City	Nara
Period	27/11/13 → 29/11/13

Bibliographical note

Publisher Copyright:
© 2013 Proceedings of the 13th International Conference on Parsing Technologies, IWPT 2013. All rights reserved.

Cite this

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Dynamic-oracle transition-based parsing with calibrated probabilistic output

Abstract

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Dynamic-oracle transition-based parsing with calibrated probabilistic output

Abstract

Conference

Bibliographical note

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Conference Contributed

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