Exploring self training for Hindi dependency parsing

In this paper we explore the effect of selftraining on Hindi dependency parsing. We consider a state-of-the-art Hindi dependency parser and apply self-training by using a large raw corpus. We consider two types of raw corpus, one from same domain as of training and testing data and the other from different domain. We also do an experiment, where we add small gold-standard data to the training set. Comparing these experiments, we show the impact of adding small, but gold-standard data to training data versus large, but automatically parsed data on Hindi parser.