# Cross-lingual, Character-Level Neural Morphological Tagging

arXiv: https://arxiv.org/abs/1708.09157 · HTML: https://mlsanity.com/paper/1708.09157/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Aug 2017.

## Built on by (most cited first, 17 of 17)

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- [On the Role of Morphological Information for Contextual Lemmatization](https://mlsanity.com/paper/2302.00407/) (2023)
- [Computational Morphology with Neural Network Approaches](https://mlsanity.com/paper/2105.09404/) (2021)
- [Recent advancements in computational morphology : A comprehensive survey](https://mlsanity.com/paper/2406.05424/) (2024)
- [Feature Aggregation in Zero-Shot Cross-Lingual Transfer Using Multilingual BERT](https://mlsanity.com/paper/2205.08497/) (2022)
- [Improving Polish to English Neural Machine Translation with Transfer Learning: Effects of Data Volume and Language Similarity](https://mlsanity.com/paper/2306.00660/) (2023)
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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.