A Joint Multitask Model for Morpho-Syntactic Parsing

• URL: http://arxiv.org/abs/2508.14307v1
• Date: Tue, 19 Aug 2025 23:09:36 GMT
• Title: A Joint Multitask Model for Morpho-Syntactic Parsing
• Authors: Demian Inostroza, Mel Mistica, Ekaterina Vylomova, Chris Guest, Kemal Kurniawan,
• Abstract summary: We present a joint multitask model for the UniDive 2025 Morpho-Syntactic Parsing shared task.<n>Our system uses a shared XLM-RoBERTa encoder with three specialized decoders for content word identification, dependency parsing, and morphosyntactic feature prediction.
• Score: 2.265421354393977
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

Related papers

• Binary Token-Level Classification with DeBERTa for All-Type MWE Identification: A Lightweight Approach with Linguistic Enhancement [1.8429656136522097]
  We present a comprehensive approach for multiword expression (MWE) identification that combines binary token-level classification, linguistic feature integration, and data augmentation.<n>Our DeBERTa-v3-large model achieves 69.8% F1 on the CoAM dataset, surpassing the best results (Qwen-72B, 57.8% F1) on this dataset by 12 points while using 165x fewer parameters.
  arXiv  Detail & Related papers  (2026-01-27T08:42:54Z)
• Llama-Embed-Nemotron-8B: A Universal Text Embedding Model for Multilingual and Cross-Lingual Tasks [4.739000717606982]
  llama-embed-nemotron-8b is an open-weights text embedding model.<n>It achieves state-of-the-art performance on the Multilingual Massive Text Embedding Benchmark.
  arXiv  Detail & Related papers  (2025-11-10T12:13:16Z)
• Enhancing Spatio-Temporal Zero-shot Action Recognition with Language-driven Description Attributes [54.50887214639301]
  We propose an innovative approach that harnesses web-crawled descriptions, leveraging a large-language model to extract relevant keywords.<n>This method reduces the need for human annotators and eliminates the laborious manual process of attribute data creation.<n>In our zero-shot experiments, our model achieves accuracies of 81.0%, 53.1%, and 68.9% on UCF-101, HMDB-51, and Kinetics-600, respectively.
  arXiv  Detail & Related papers  (2025-10-31T07:45:44Z)
• Exploring Multiple Strategies to Improve Multilingual Coreference Resolution in CorefUD [0.8602553195689511]
  We present a novel end-to-end neural coreference resolution system utilizing the CorefUD 1.1 dataset.<n>The proposed model is based on the standard end-to-end neural coreference resolution system.<n>We propose several extensions to enhance performance across diverse linguistic contexts.
  arXiv  Detail & Related papers  (2024-08-29T20:27:05Z)
• Query-by-Example Keyword Spotting Using Spectral-Temporal Graph Attentive Pooling and Multi-Task Learning [11.182456667123835]
  We present a novel Query-by-Example (QbyE) KWS system that employs spectral-temporal attentive graph pooling and multi-task learning. This framework aims to effectively learn speaker-invariant and linguistic-informative embeddings for QbyE KWS tasks.
  arXiv  Detail & Related papers  (2024-08-27T03:44:57Z)
• Evaluating Large Language Models Using Contrast Sets: An Experimental Approach [0.0]
  We introduce an innovative technique for generating a contrast set for the Stanford Natural Language Inference dataset. Our strategy involves the automated substitution of verbs, adverbs, and adjectives with their synonyms to preserve the original meaning of sentences. This method aims to assess whether a model's performance is based on genuine language comprehension or simply on pattern recognition.
  arXiv  Detail & Related papers  (2024-04-02T02:03:28Z)
• UniverSLU: Universal Spoken Language Understanding for Diverse Tasks with Natural Language Instructions [64.50935101415776]
  We build a single model that jointly performs various spoken language understanding (SLU) tasks. We demonstrate the efficacy of our single multi-task learning model "UniverSLU" for 12 speech classification and sequence generation task types spanning 17 datasets and 9 languages.
  arXiv  Detail & Related papers  (2023-10-04T17:10:23Z)
• CompoundPiece: Evaluating and Improving Decompounding Performance of Language Models [77.45934004406283]
  We systematically study decompounding, the task of splitting compound words into their constituents. We introduce a dataset of 255k compound and non-compound words across 56 diverse languages obtained from Wiktionary. We introduce a novel methodology to train dedicated models for decompounding.
  arXiv  Detail & Related papers  (2023-05-23T16:32:27Z)
• An Open Dataset and Model for Language Identification [84.15194457400253]
  We present a LID model which achieves a macro-average F1 score of 0.93 and a false positive rate of 0.033 across 201 languages. We make both the model and the dataset available to the research community.
  arXiv  Detail & Related papers  (2023-05-23T08:43:42Z)
• Matcher: Segment Anything with One Shot Using All-Purpose Feature Matching [63.88319217738223]
  We present Matcher, a novel perception paradigm that utilizes off-the-shelf vision foundation models to address various perception tasks. Matcher demonstrates impressive generalization performance across various segmentation tasks, all without training. Our results further showcase the open-world generality and flexibility of Matcher when applied to images in the wild.
  arXiv  Detail & Related papers  (2023-05-22T17:59:43Z)
• NEMO: Frequentist Inference Approach to Constrained Linguistic Typology Feature Prediction in SIGTYP 2020 Shared Task [83.43738174234053]
  We employ frequentist inference to represent correlations between typological features and use this representation to train simple multi-class estimators that predict individual features. Our best configuration achieved the micro-averaged accuracy score of 0.66 on 149 test languages.
  arXiv  Detail & Related papers  (2020-10-12T19:25:43Z)
• FiSSA at SemEval-2020 Task 9: Fine-tuned For Feelings [2.362412515574206]
  In this paper, we present our approach for sentiment classification on Spanish-English code-mixed social media data. We explore both monolingual and multilingual models with the standard fine-tuning method. Although two-step fine-tuning improves sentiment classification performance over the base model, the large multilingual XLM-RoBERTa model achieves best weighted F1-score.
  arXiv  Detail & Related papers  (2020-07-24T14:48:27Z)
• Learning to Learn Morphological Inflection for Resource-Poor Languages [105.11499402984482]
  We propose to cast the task of morphological inflection - mapping a lemma to an indicated inflected form - for resource-poor languages as a meta-learning problem. Treating each language as a separate task, we use data from high-resource source languages to learn a set of model parameters. Experiments with two model architectures on 29 target languages from 3 families show that our suggested approach outperforms all baselines.
  arXiv  Detail & Related papers  (2020-04-28T05:13:17Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.