Universal Dependencies Parsing for Colloquial Singaporean English

Hongmin Wang, Yue Zhang, GuangYong Leonard Chan, Jie Yang, Hai Leong Chieu


Abstract
Singlish can be interesting to the ACL community both linguistically as a major creole based on English, and computationally for information extraction and sentiment analysis of regional social media. We investigate dependency parsing of Singlish by constructing a dependency treebank under the Universal Dependencies scheme, and then training a neural network model by integrating English syntactic knowledge into a state-of-the-art parser trained on the Singlish treebank. Results show that English knowledge can lead to 25% relative error reduction, resulting in a parser of 84.47% accuracies. To the best of our knowledge, we are the first to use neural stacking to improve cross-lingual dependency parsing on low-resource languages. We make both our annotation and parser available for further research.
Anthology ID:
P17-1159
Volume:
Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
Month:
July
Year:
2017
Address:
Vancouver, Canada
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
1732–1744
Language:
URL:
https://www.aclweb.org/anthology/P17-1159
DOI:
10.18653/v1/P17-1159
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PDF:
http://aclanthology.lst.uni-saarland.de/P17-1159.pdf