Universal Dependencies Parsing for Colloquial Singaporean English
Hongmin Wang, Yue Zhang, GuangYong Leonard Chan, Jie Yang, Hai Leong Chieu · 2017
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-ofthe-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.