mlsanityData to 7 Oct 2026

Cross-lingual, Character-Level Neural Morphological Tagging

arXiv 1708.09157 ↗First posted 30 Aug 2017cs.CLcited by 17

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Abstract (arXiv)

Even for common NLP tasks, sufficient supervision is not available in many languages -- morphological tagging is no exception. In the work presented here, we explore a transfer learning scheme, whereby we train character-level recurrent neural taggers to predict morphological taggings for high-resource languages and low-resource languages together. Learning joint character representations among multiple related languages successfully enables knowledge transfer from the high-resource languages to the low-resource ones, improving accuracy by up to 30% over a monolingual model.

Key facts. Cited by 17 papers in the map (data through 7 Oct 2026). First posted on arXiv 30 Aug 2017.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 5; 2022: 6; 2023: 3; 2024: 1; 2025: 0; 2026: 2.

17 papers.

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  1. 2021An Empirical Survey of Data Augmentation for Limited Data Learning in NLPcited by 101
  2. 2021Revisiting the Primacy of English in Zero-shot Cross-lingual Transfercited by 47
  3. 2021Multilingual Multimodal Pre-training for Zero-Shot Cross-Lingual Transfer of Vision-Language Modelscited by 41
  4. 2023Zero-shot cross-lingual transfer language selection using linguistic similaritycited by 28
  5. 2022Evaluating the Diversity, Equity and Inclusion of NLP Technology: A Case Study for Indian Languagescited by 20
  6. 2021A Dataset and Baselines for Multilingual Reply Suggestioncited by 9
  7. 2022Transfer Language Selection for Zero-Shot Cross-Lingual Abusive Language Detectioncited by 8
  8. 2022Morphological Processing of Low-Resource Languages: Where We Are and What's Nextcited by 4
  9. 2022Graph-Based Multilingual Label Propagation for Low-Resource Part-of-Speech Taggingcited by 4
  10. 2022CaMEL: Case Marker Extraction without Labelscited by 3
  11. 2023On the Role of Morphological Information for Contextual Lemmatizationcited by 3
  12. 2021Computational Morphology with Neural Network Approachescited by 2
5 more
  1. 2024Recent advancements in computational morphology : A comprehensive surveycited by 2
  2. 2022Feature Aggregation in Zero-Shot Cross-Lingual Transfer Using Multilingual BERTcited by 1
  3. 2023Improving Polish to English Neural Machine Translation with Transfer Learning: Effects of Data Volume and Language Similaritycited by 1
  4. 2026Toward a Cross-Lingual Romanization Ecosystem for Sinitic Languages: A Paired Mandarin-Cantonese Case Studycited by 1
  5. 2026Hybrid Neural-LLM Pipeline for Morphological Glossing in Endangered Language Documentation: A Case Study of Jungar Tuvancited by 0