Hierarchical Low-Rank Tensors for Multilingual Transfer Parsing
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Hierarchical low-rank.pdf
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Author(s)
Barzilay, Regina
Zhang, Yuan
Date Issued
September 2015
Journal
Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing
Publisher
Association for Computational Linguistics
Citation
Zhang, Yuan and Regina Barzilay. "Hierarchical Low-Rank Tensors for Multilingual Transfer Parsing." 2015 Conference on Empirical Methods in Natural Language Processing, Lisbon, Portugal, 17-21 September, 2015. Association for Computational Linguistics, 2015, pp. 1857–1867.
Version
Author's final manuscript
Abstract
Accurate multilingual transfer parsing typically relies on careful feature engineering. In this paper, we propose a hierarchical tensor-based approach for this task. This approach induces a compact feature representation by combining atomic features. However, unlike traditional tensor models, it enables us to incorporate prior knowledge about desired feature interactions, eliminating invalid feature combinations. To this end, we use a hierarchical
structure that uses intermediate embeddings to capture desired feature combinations. Algebraically, this hierarchical tensor is equivalent to the sum of traditional tensors with shared components, and thus can be effectively trained with standard online algorithms. In both unsupervised and semi-supervised transfer scenarios, our hierarchical tensor consistently improves UAS and LAS over state-of-the-art multilingual transfer parsers and the traditional tensor model across 10 different languages.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
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
http://www.emnlp2015.org/proceedings/EMNLP/
