arXiv AI September 25, 2026 By Toqeer Ehsan, Miriam Butt, Sarmad Hussain, Hassan Alhuzali, Ali Al-Laith

Multi-Task Learning by using Contextualized Word Representations for Syntactic Parsing of a Morphologically Rich Language

The paper tackles syntactic parsing for Urdu, a morphologically rich language, and reports state‑of‑the‑art results for both constituency and dependency parsing. It introduces four key contributions: converting the CLE‑UTB phrase structure treebank into a dependency treebank with language‑specific mapping rules, a novel sequence labeling scheme that unifies the parsing task, training contextualized word representations on a 220‑million‑token Urdu corpus, and a parsing framework that employs both single‑task and multi‑task learning. Experiments show that the multi‑task setup boosts performance, achieving an F1 score of 91.39 for constituency parsing and a labeled attachment score of 85.69 for dependency parsing, improving over previous results.

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arXiv:2606. 07167v1 Announce Type: cross Abstract: Meaningful multilingual evaluation must test models in the target language and educational context.

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llms rag nlp fine-tuning

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