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Multi-Task Learning by using Contextualized Word Representations for Syntactic Parsing of a Morphologically Rich Language
We address the challenge of syntactic parsing for Urdu, a morphologically rich language, and present state-of-the-art results for both constituency and dependency parsing. This paper offers four major contributions: 1) the conversion of the CLE-UTB phrase structure treebank into a dependency treeban
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