# Cross-Lingual Constituency Parsing for Middle High German: A Delexicalized Approach

arXiv: https://arxiv.org/abs/2308.04645 · HTML: https://mlsanity.com/paper/2308.04645/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 46 references that are in the map.
- First posted on arXiv 9 Aug 2023.

## Builds on (most cited first, 15 of 15)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [XTREME: A Massively Multilingual Multi-task Benchmark for Evaluating Cross-lingual Generalization](https://mlsanity.com/paper/2003.11080/) (2020)
- [Massively Multilingual Sentence Embeddings for Zero-Shot Cross-Lingual Transfer and Beyond](https://mlsanity.com/paper/1812.10464/) (2018)
- [Constituency Parsing with a Self-Attentive Encoder](https://mlsanity.com/paper/1805.01052/) (2018)
- [Cross-Lingual Ability of Multilingual BERT: An Empirical Study](https://mlsanity.com/paper/1912.07840/) (2019)
- [German's Next Language Model](https://mlsanity.com/paper/2010.10906/) (2020)
- [Multilingual Constituency Parsing with Self-Attention and Pre-Training](https://mlsanity.com/paper/1812.11760/) (2018)
- [A Minimal Span-Based Neural Constituency Parser](https://mlsanity.com/paper/1705.03919/) (2017)
- [Many Languages, One Parser](https://mlsanity.com/paper/1602.01595/) (2016)
- [What's Going On in Neural Constituency Parsers? An Analysis](https://mlsanity.com/paper/1804.07853/) (2018)
- [Span-Based Constituency Parsing with a Structure-Label System and Provably Optimal Dynamic Oracles](https://mlsanity.com/paper/1612.06475/) (2016)
- [Tetra-Tagging: Word-Synchronous Parsing with Linear-Time Inference](https://mlsanity.com/paper/1904.09745/) (2019)
- [Cross-Lingual Retrieval Augmented Prompt for Low-Resource Languages](https://mlsanity.com/paper/2212.09651/) (2022)
- [MulZDG: Multilingual Code-Switching Framework for Zero-shot Dialogue Generation](https://mlsanity.com/paper/2208.08629/) (2022)
- [Sentence Constituent-Aware Aspect-Category Sentiment Analysis with Graph Attention Networks](https://mlsanity.com/paper/2010.01461/) (2020)

## Built on by (most cited first, 2 of 2)

- [Is text normalization relevant for classifying medieval charters?](https://mlsanity.com/paper/2408.16446/) (2024)
- [Lexicalized Constituency Parsing for Middle Dutch: Low-resource Training and Cross-Domain Generalization](https://mlsanity.com/paper/2601.07008/) (2026)

## Abstract (arXiv)

Constituency parsing plays a fundamental role in advancing natural language processing (NLP) tasks. However, training an automatic syntactic analysis system for ancient languages solely relying on annotated parse data is a formidable task due to the inherent challenges in building treebanks for such languages. It demands extensive linguistic expertise, leading to a scarcity of available resources. To overcome this hurdle, cross-lingual transfer techniques which require minimal or even no annotated data for low-resource target languages offer a promising solution. In this study, we focus on building a constituency parser for Middle High German (MHG) under realistic conditions, where no annotated MHG treebank is available for training. In our approach, we leverage the linguistic continuity and structural similarity between MHG and Modern German (MG), along with the abundance of MG treebank resources. Specifically, by employing the mathitdelexicalization method, we train a constituency parser on MG parse datasets and perform cross-lingual transfer to MHG parsing. Our delexicalized constituency parser demonstrates remarkable performance on the MHG test set, achieving an F1-score of 67.3%. It outperforms the best zero-shot cross-lingual baseline by a margin of 28.6% points. These encouraging results underscore the practicality and potential for automatic syntactic analysis in other ancient languages that face similar challenges as MHG.