mlsanityData to 7 Oct 2026

data2lang2vec: Data Driven Typological Features Completion

arXiv 2409.17373 ↗First posted 25 Sep 2024cs.CLcited by 4

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Abstract (arXiv)

Language typology databases enhance multi-lingual Natural Language Processing (NLP) by improving model adaptability to diverse linguistic structures. The widely-used lang2vec toolkit integrates several such databases, but its coverage remains limited at 28.9%. Previous work on automatically increasing coverage predicts missing values based on features from other languages or focuses on single features, we propose to use textual data for better-informed feature prediction. To this end, we introduce a multi-lingual Part-of-Speech (POS) tagger, achieving over 70% accuracy across 1,749 languages, and experiment with external statistical features and a variety of machine learning algorithms. We also introduce a more realistic evaluation setup, focusing on likely to be missing typology features, and show that our approach outperforms previous work in both setups.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 14 of its 28 references that are in the map. First posted on arXiv 25 Sep 2024.

Builds on

14 of 28 references.

References · 14

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
  2. 2019Unsupervised Cross-lingual Representation Learning at Scalecited by 3,907
  3. 2019Optuna: A Next-generation Hyperparameter Optimization Frameworkcited by 2,455
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  5. 2020The State and Fate of Linguistic Diversity and Inclusion in the NLP Worldcited by 789
  6. 2020LUKE: Deep Contextualized Entity Representations with Entity-aware Self-attentioncited by 326
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  9. 2020Massive Choice, Ample Tasks (MaChAmp): A Toolkit for Multi-task Learning in NLPcited by 50
  10. 2022TwHIN: Embedding the Twitter Heterogeneous Information Network for Personalized Recommendationcited by 25
  11. 2023GlotScript: A Resource and Tool for Low Resource Writing System Identificationcited by 15
  12. 2021Language Embeddings for Typology and Cross-lingual Transfer Learningcited by 8
2 more
  1. 2024A Reproducibility Study on Quantifying Language Similarity: The Impact of Missing Values in the URIEL Knowledge Basecited by 7
  2. 2020Predicting Typological Features in WALS using Language Embeddings and Conditional Probabilities: 'UFAL Submission to the SIGTYP 2020 Shared Taskcited by 6

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 2; 2026: 2.

4 papers.

  1. 2025Analyzing the Effect of Linguistic Similarity on Cross-Lingual Transfer: Tasks and Experimental Setups Mattercited by 10
  2. 2025Limited-Resource Adapters Are Regularizers, Not Linguistscited by 0
  3. 2026Typological Feature Prediction with Large Language Models: An In-Context Learning Approachcited by 0
  4. 2026Self-Supervised Lexical Representation Learning for Fast, Large-Scale Phylogenetic Inferencecited by 0