Transfer Learning for Constituency-Based Grammars
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Barzilay_Transfer learning.pdf
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376.05 KB
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Author(s)
Zhang, Yuan
Barzilay, Regina
Globerson, Amir
Date Issued
August 2013
Journal
Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics
Publisher
Association for Computational Linguistics
Citation
Zhang, Yuan, Regina Barzilay, and Amir Globerson. "Transfer Learning for Constituency-Based Grammars." 51st Annual Meeting of the Association for Computational Linguistics (August 2013).
Version
Author's final manuscript
Abstract
In this paper, we consider the problem of cross-formalism transfer in parsing. We are interested in parsing constituency-based grammars such as HPSG and CCG using a small amount of data specific for the target formalism, and a large quantity of coarse CFG annotations from the Penn Treebank. While all of the target formalisms share a similar basic syntactic structure with Penn Treebank CFG, they also encode additional constraints and semantic features. To handle this apparent discrepancy, we design a probabilistic model that jointly generates CFG and target formalism parses. The model includes features of both parses, allowing transfer between the formalisms, while preserving parsing efficiency. We evaluate our approach on three constituency-based grammars — CCG, HPSG, and LFG, augmented with the Penn Treebank-1. Our experiments show that across all three formalisms, the target parsers significantly benefit from the coarse annotations.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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Creative Commons Attribution-Noncommercial-Share Alike
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DOI of Published Version
https://aclweb.org/anthology/P/P13/
