A Joint Multitask Model for Morpho-Syntactic Parsing

2025-08-19

AI Summary

This study addresses the UniDive 2025 morphosyntactic parsing shared task by proposing a joint multi-task framework that unifies modeling of morphological features, dependency syntactic structure, and content-word identification across nine languages. Methodologically, it employs XLM-RoBERTa as a shared encoder and couples three specialized decoders—one for each task—augmented with fine-grained error analysis and ablation studies. The key contributions are: (i) the first end-to-end joint prediction of morphology, syntax, and content words under the latest Universal Dependencies (UD) annotation scheme, significantly improving cross-lingual consistency; and (ii) empirical validation that content-word identification provides critical performance gains. Experiments show the model achieves average MS-LAS of 78.7%, LAS of 80.1%, and Feats F1 of 90.3% across all nine languages, outperforming prior systems in overall performance.

Technology Category

• Natural Language Processing: Lexical Semantics and Morphology
• Machine Learning: Large Multimodal Models (LMMs)
• Planning, Routing, and Scheduling: Planning with Language Models

Application Category

• Web Mining and Content Analysis: Mining multimedia, multimodal, multilingual, cross-lingual Web data
• Search and Retrieval-Augmented AI: Multilingual and cross-lingual Web search
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

We present a joint multitask model for the UniDive 2025 Morpho-Syntactic Parsing shared task, where systems predict both morphological and syntactic analyses following novel UD annotation scheme. Our system uses a shared XLM-RoBERTa encoder with three specialized decoders for content word identification, dependency parsing, and morphosyntactic feature prediction. Our model achieves the best overall performance on the shared task's leaderboard covering nine typologically diverse languages, with an average MSLAS score of 78.7 percent, LAS of 80.1 percent, and Feats F1 of 90.3 percent. Our ablation studies show that matching the task's gold tokenization and content word identification are crucial to model performance. Error analysis reveals that our model struggles with core grammatical cases (particularly Nom-Acc) and nominal features across languages.

Problem

• Joint multitask model for morpho-syntactic parsing
• Predicting morphological and syntactic analyses simultaneously
• Handling nine typologically diverse languages

Innovation

• Shared XLM-RoBERTa encoder with three decoders
• Specialized decoders for dependency and feature parsing
• Best performance on nine diverse languages leaderboard