# MRL Parsing Without Tears: The Case of Hebrew

arXiv: https://arxiv.org/abs/2403.06970 · HTML: https://mlsanity.com/paper/2403.06970/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 23 references that are in the map.
- First posted on arXiv 11 Mar 2024.

## Builds on (most cited first, 10 of 10)

- [Stanza: A Python Natural Language Processing Toolkit for Many Human Languages](https://mlsanity.com/paper/2003.07082/) (2020)
- [Trankit: A Light-Weight Transformer-based Toolkit for Multilingual Natural Language Processing](https://mlsanity.com/paper/2101.03289/) (2021)
- [Simple and Accurate Dependency Parsing Using Bidirectional LSTM Feature Representations](https://mlsanity.com/paper/1603.04351/) (2016)
- [AlephBERT:A Hebrew Large Pre-Trained Language Model to Start-off your Hebrew NLP Application With](https://mlsanity.com/paper/2104.04052/) (2021)
- [Neural Modeling for Named Entities and Morphology (NEMO^2)](https://mlsanity.com/paper/2007.15620/) (2020)
- [DictaBERT: A State-of-the-Art BERT Suite for Modern Hebrew](https://mlsanity.com/paper/2308.16687/) (2023)
- [What's Wrong with Hebrew NLP? And How to Make it Right](https://mlsanity.com/paper/1908.05453/) (2019)
- [Large Pre-Trained Models with Extra-Large Vocabularies: A Contrastive Analysis of Hebrew BERT Models and a New One to Outperform Them All](https://mlsanity.com/paper/2211.15199/) (2022)
- [A Truly Joint Neural Architecture for Segmentation and Parsing](https://mlsanity.com/paper/2402.02564/) (2024)
- [Multilingual Sequence-to-Sequence Models for Hebrew NLP](https://mlsanity.com/paper/2212.09682/) (2022)

## Built on by (most cited first, 3 of 3)

- [Splintering Nonconcatenative Languages for Better Tokenization](https://mlsanity.com/paper/2503.14433/) (2025)
- [NeoDictaBERT: Pushing the Frontier of BERT models for Hebrew](https://mlsanity.com/paper/2510.20386/) (2025)
- [MiqraBERT: Regression-Based Sentence-BERT Finetuning for Biblical Hebrew Parallel Detection](https://mlsanity.com/paper/2606.19638/) (2026)

## Abstract (arXiv)

Syntactic parsing remains a critical tool for relation extraction and information extraction, especially in resource-scarce languages where LLMs are lacking. Yet in morphologically rich languages (MRLs), where parsers need to identify multiple lexical units in each token, existing systems suffer in latency and setup complexity. Some use a pipeline to peel away the layers: first segmentation, then morphology tagging, and then syntax parsing; however, errors in earlier layers are then propagated forward. Others use a joint architecture to evaluate all permutations at once; while this improves accuracy, it is notoriously slow. In contrast, and taking Hebrew as a test case, we present a new "flipped pipeline": decisions are made directly on the whole-token units by expert classifiers, each one dedicated to one specific task. The classifiers are independent of one another, and only at the end do we synthesize their predictions. This blazingly fast approach sets a new SOTA in Hebrew POS tagging and dependency parsing, while also reaching near-SOTA performance on other Hebrew NLP tasks. Because our architecture does not rely on any language-specific resources, it can serve as a model to develop similar parsers for other MRLs.