Typology-Aware Multilingual Morphosyntactic Parsing with Joint Abstract Node Modeling

ACL ARR 2026 January Submission9556 Authors

Keywords: morphosyntactic parsing, abstract node modeling, multilingual dependency parsing, typology-aware adaptation, non-overt syntax

Abstract: The UniDive 2025 Morphosyntactic Parsing (MSP) shared task introduces a representation unifying dependency structure, morphological features, and unrealized arguments. Unlike Universal Dependencies, MSP encodes abstract nodes (e.g., dropped subjects, implicit pronouns) as labels projected onto content words, which standard UD parsers cannot model. We present a multilingual, typology-aware joint system integrating word-type prediction, content-only parsing, morphological tagging, and an abstract-node component within a single architecture. The model combines the baseline joint framework with typology-conditioned adapters and progressive weighting for abstract supervision. On the MSP test set, our model outperforms the leading submission by 3.23 percentage points in MSLAS, 3.35 in LAS, and 1.78 in FEATS macro F1, demonstrating the effectiveness of typology-sensitive multi-task learning in MSP.

Paper Type: Long

Research Area: Hierarchical Structure Prediction, Syntax, and Parsing

Research Area Keywords: Syntax: Tagging, Chunking and Parsing; Morphology and Word Segmentation; Multilingualism and Cross-Lingual NLP; Structured Prediction; Machine Learning for NLP; Representation Learning

Contribution Types: NLP engineering experiment, Approaches to low-resource settings, Publicly available software and/or pre-trained models

Languages Studied: Czech, English, Hebrew, Italian, Polish, Portuguese, Serbian, Swedish, Turkish

Submission Number: 9556