  We investigate the problem of parsing conversational data of
morphologically-rich languages such as Hindi where argument scrambling occurs
frequently. We evaluate a state-of-the-art non-linear transition-based parsing
system on a new dataset containing 506 dependency trees for sentences from
Bollywood (Hindi) movie scripts and Twitter posts of Hindi monolingual
speakers. We show that a dependency parser trained on a newswire treebank is
strongly biased towards the canonical structures and degrades when applied to
conversational data. Inspired by Transformational Generative Grammar, we
mitigate the sampling bias by generating all theoretically possible alternative
word orders of a clause from the existing (kernel) structures in the treebank.
Training our parser on canonical and transformed structures improves
performance on conversational data by around 9% LAS over the baseline newswire
parser.
