Joining Hands: Exploiting Monolingual Treebanks for Parsing of Code-mixing Data

Irshad Ahmad Bhat, Riyaz Ahmad Bhat, Manish Shrivastava, Dipti Misra Sharma

In this paper, we propose efficient and less resource-intensive strategies for parsing of code-mixed data. These strategies are not constrained by in-domain annotations, rather they leverage pre-existing monolingual annotated resources for training. We show that these methods can produce significantly better results as compared to an informed baseline. Besides, we also present a data set of 450 Hindi and English code-mixed tweets of Hindi multilingual speakers for evaluation. The data set is manually annotated with Universal Dependencies.

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