Typology-aware Multilingual Morphosyntactic Parsing with Functional Node Filtering (2025)

Abstract: This paper presents a system for the UniDive Morphosyntactic Parsing (MSP) Shared Task, where it ranked second overall among participating teams. The task introduces a morphosyntactic representation that jointly models syntactic dependencies and morphological features by treating content-bearing elements as graph nodes and encoding functional elements as feature annotations, posing challenges for conventional parsers and necessitating more flexible, linguistically informed approaches. The proposed system combines a typology-aware, multitask parser with a multilingual content/function classifier to handle structural variance across languages. The architecture uses adapter modules and language embeddings to encode typological information. Evaluations across 9 typologically varied languages confirm that the system can accurately replicate both universal and language-specific morphosyntactic patterns.

## introduction morphosyntactic parsing aspires to integrate syntactic structure with fine-grained morphological annotation to offer a deeper and linguistically neutral understanding of sentence structure. the unidive morphosyntactic parsing (msp) shared task offers a novel paradigm that challenges conventional parsing assumptions by restructuring dependency trees around content-bearing elements and functional grammatical units. in this new schema, only content nodes-such as lexical verbs, nouns, and adjectives-are represented explicitly in the graph, while functional elements like auxiliaries, clitics, and determiners are removed and represented as features of the content words. moreover, the format integrates abstract nodes for dropped or elided arguments, which are syntactically required but not present on the surface, as commonly seen in pro-drop languages. such a shift from surface-token-based syntax to deeper morphosyntactic abstraction makes this task both linguistically rich and technically challenging. traditional parsers must be adapted to filter out functional nodes and accommodate missing heads, necessitating new modeling strategies. in response to the novel task format, this study adapts the udapter model @xcite , a typologically informed multilingual dependency parser. the original architecture is extended with a content/function classifier and decoding routines are modified accordingly, while multitask learning is leveraged for both dependency parsing and morphological tagging. this approach not only conforms to the structural assumptions of the msp task but also exploits cross-lingual signals across 9 diverse languages. evaluated in the official shared task, the proposed system ranks second overall. as the first adaptation of udapter to the msp framework, it introduces a content/function classifier to align parsing with the task's structure. by combining multilingual pretraining, typological conditioning, and multitask learning, the system effectively integrates syntax and morphology beyond surface-level representations, offering a robust solution for typologically diverse parsing. this paper is structured as follows: section 2 reviews related work on dependency parsing and morphosyntactic modeling. section 3 presents the system architecture. section 4 details the experimental setup and results. section 5 concludes the paper and outlines directions for future research. ## related work dependency parsing methods are traditionally grouped into two paradigms: transition-based and graph-based approaches. transition-based parsers, such as those by @xcite ; @xcite ; @xcite , incrementally construct depen-dency trees through local decisions. these models are computationally efficient but often suffer from error propagation. a significant advancement in this line was the biaffine parser of @xcite , built on @xcite , which introduced attention-based arc and label scoring and achieved state-of-the-art results across many languages. multilingual dependency parsing has gained traction due to the universal dependencies (ud) framework @xcite , which standardizes syntactic annotation across more than 100 languages. multilingual benchmarks enabled by ud treebanks include conll-x @xcite , conll 2007 @xcite , and @xcite . full parsing pipelines from raw text to dependency structures in 75 languages were evaluated in the conll 2018 shared task. modern approaches increasingly rely on multilingual pretrained language models like mbert @xcite and xlm-roberta @xcite . udify @xcite was among the first to use mbert for joint multitask prediction of pos tags, morphological features, lemmas, and dependencies across all ud languages. its universal parameter sharing, however, made it less flexible for languages with low resources or typological distance. by introducing lightweight adaptor modules in between mbert layers, udapter @xcite addressed this issue and preserved the advantages of multilingual pretraining while enabling typologyaware, language-specific transformation. this approach improved generalization, especially when resources were scarce or the setting is zero shot. the morphosyntactic parsing (msp) shared task @xcite presents an updated parsing approach in which function words are appended as morphological features and only content words are represented as syntactic nodes. abstract nodes are also integrated to represent dropped or implicit arguments, such as pro-drop pronouns. this annotation strategy decouples word segmentation from syntactic structure, enabling more typologically robust morphosyntactic parsing. ## system architecture the system integrates a multilingual content/function classifier with a universal dependency parser to handle the structural transformations introduced by the msp shared task. in this format, functional nodes-such as determiners, auxiliaries, adpositions, and punctuation-are excluded from the dependency graph by assigning them null heads, rendering them incompatible with standard parsing methods. as illustrated in figure 1 , adpositions like sonra and clitic constructions like -kine are not linked via dependency arcs. instead, their morphological contributions are absorbed into the parent node: for instance, the combination gittikten sonra alters the original @xmath0, and sizin + -kine merges into a single node with @xmath1;dat. to support this abstraction, a bert-based classifier is applied in preprocessing to identify and remove functional tokens before parsing. the udapter model, equipped with typology-aware adapters and multitask heads for both morphological tagging and dependency parsing, then processes the remaining content nodes under this structurally modified scheme. ## experimental setup & results in this section, the multilingual parsing system's test set results, evaluation methods, and training configuration are shown. key hyperparameters, implementation details, and necessary preparation steps for the msp shared task format are explained. three metrics-mslas, las, and morphological feature (feats) f1-are used to compare the empirical performance across languages in the multilingual and monolingual setups. 29 ## conclusion this work presents the first successful adaptation of the udapter model to the unidive msp shared task, which challenges traditional parsing by introducing structurally flexible and typologically informed dependency representations. the task format-featuring abstract nodes and functional node eliminations-necessitates substantial revisions to conventional parsing pipelines. to address these challenges, the proposed system combines a bert-based functional node classifier (cf-bert) with udapter's multilingual adapter architecture and factored multitask decoders. cf-bert aligns training and test conditions by filtering out non-content elements with near-perfect accuracy, allowing the parser to focus exclusively on content-bearing structures. this setup enhances both syntactic and morphological prediction under cross-lingual supervision. experimental results show that the multilingual system consistently outperforms monolingual baselines across mslas, las, and feats f1 metrics, with particularly strong gains in morphologically rich languages like turkish and typologically adjacent languages like italian and serbian. the results affirm the system's core strengths: typology-aware representation, functional node filtering, and multitask structural learning. future improvements may focus on integrating abstract node generation to better capture pro-drop phenomena and further enhance recall in structurally underspecified contexts.