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Target Language Adaptation of Discriminative Transfer Parsers
Number of Authors: 3
2013 (English)Conference paper (Refereed)
Abstract [en]

We study multi-source transfer parsing for resource-poor target languages; specifically methods for target language adaptation of delexicalized discriminative graph-based dependency parsers. We first show how recent insights on selective parameter sharing, based on typological and language-family features, can be applied to a discriminative parser by carefully decomposing its model features. We then show how the parser can be relexicalized and adapted using unlabeled target language data and a learning method that can incorporate diverse knowledge sources through ambiguous labelings. In the latter scenario, we exploit two sources of knowledge: arc marginals derived from the base parser in a self-training algorithm, and arc predictions from multiple transfer parsers in an ensemble-training algorithm. Our final model outperforms the state of the art in multi-source transfer parsing on 15 out of 16 evaluated languages.

Place, publisher, year, edition, pages
2013, 13.
National Category
Computer and Information Science
URN: urn:nbn:se:ri:diva-15375OAI: diva2:1036692
The 2013 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
Available from: 2016-10-13 Created: 2016-10-13

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