# From SPMRL to NMRL: What Did We Learn (and Unlearn) in a Decade of Parsing Morphologically-Rich Languages (MRLs)?

arXiv: https://arxiv.org/abs/2005.01330 · HTML: https://mlsanity.com/paper/2005.01330/

## Key facts

- Cited by 26 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 May 2020.

## Built on by (most cited first, 26 of 26)

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- [Do Pretrained Contextual Language Models Distinguish between Hebrew Homograph Analyses?](https://mlsanity.com/paper/2405.07099/) (2024)
- [From Text to Graph: Leveraging Graph Neural Networks for Enhanced Explainability in NLP](https://mlsanity.com/paper/2504.02064/) (2025)
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- [Leveraging Digitized Newspapers to Collect Summarization Data in Low-Resource Languages](https://mlsanity.com/paper/2511.14598/) (2025)
- [When Similar Means Different: Evaluating LLMs on Arabic--Hebrew Cognates](https://mlsanity.com/paper/2606.13218/) (2026)

## Abstract (arXiv)

It has been exactly a decade since the first establishment of SPMRL, a research initiative unifying multiple research efforts to address the peculiar challenges of Statistical Parsing for Morphologically-Rich Languages (MRLs).Here we reflect on parsing MRLs in that decade, highlight the solutions and lessons learned for the architectural, modeling and lexical challenges in the pre-neural era, and argue that similar challenges re-emerge in neural architectures for MRLs. We then aim to offer a climax, suggesting that incorporating symbolic ideas proposed in SPMRL terms into nowadays neural architectures has the potential to push NLP for MRLs to a new level. We sketch strategies for designing Neural Models for MRLs (NMRL), and showcase preliminary support for these strategies via investigating the task of multi-tagging in Hebrew, a morphologically-rich, high-fusion, language