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

2026-09-24

AI Summary

This study addresses the challenges of syntactic parsing for Urdu, a morphologically rich language, by proposing a multi-task learning framework based on unified sequence labeling. Methodologically, it introduces novel conversion rules for dependency treebanks to unify constituent and dependency parsing paradigms. The approach incorporates contextualized word representations via pretraining to enable cross-task representation sharing, complemented by post-processing rules for further optimization. Experimental results demonstrate that the proposed model achieves an F1 score of 91.39 for constituent parsing and a labeled attachment score (LAS) of 85.69 for dependency parsing, significantly establishing new state-of-the-art records for Urdu syntactic parsing. Overall, this work provides an efficient solution for joint syntactic parsing in low-resource, morphologically rich languages.

Abstract

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 multi-task learning. Several post-processing rules are applied to improve the quality of the automatically converted dependency structure treebank. The proposed sequence labeling scheme enables the use of a shared architecture that learns the syntactic structures from both grammatical structures simultaneously and hence improves generalization. Experiments show that the multi-task learning setup significantly enhances parsing performance, achieving an F1 score of 91.39 for constituency parsing (an improvement of 3.29 points) and a labeled attachment score of 85.69 for dependency parsing (an improvement of 1.49 points). These results demonstrate that learning cross-task representations provides measurable benefits and advances the state of syntactic parsing for Urdu.

Problem

• Syntactic Parsing
• Urdu
• Morphologically Rich Language
• Constituency Parsing
• Dependency Parsing

Innovation

• Multi-Task Learning
• Contextualized Word Representations
• Syntactic Parsing
• Sequence Labeling
• Morphologically Rich Language