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 treebank by developing language-specific head-word and phrase-to-dependency label mapping rules; 2) a novel sequence labeling scheme that transforms the parsing task into a unified representation; 3) the training of contextualized word representations on a large 220 million tokens Urdu corpus collected from the web; and 4) development of parsing framework using two learning paradigms, single-task and ...
| Authors | Toqeer Ehsan, Miriam Butt, Sarmad Hussain et al. |
|---|---|
| Primary category | cs.CL |
| Published | 24 September 2026 |
| Last updated at source | 24 September 2026 |
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