# Syntactic Language Change in English and German: Metrics, Parsers, and Convergences

arXiv: https://arxiv.org/abs/2402.11549 · HTML: https://mlsanity.com/paper/2402.11549/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 26 of its 111 references that are in the map.
- First posted on arXiv 18 Feb 2024.

## Builds on (most cited first, 26 of 26)

- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Unsupervised Cross-lingual Representation Learning at Scale](https://mlsanity.com/paper/1911.02116/) (2019)
- [Stanza: A Python Natural Language Processing Toolkit for Many Human Languages](https://mlsanity.com/paper/2003.07082/) (2020)
- [Bidirectional LSTM-CRF Models for Sequence Tagging](https://mlsanity.com/paper/1508.01991/) (2015)
- [Deep Biaffine Attention for Neural Dependency Parsing](https://mlsanity.com/paper/1611.01734/) (2016)
- [Diachronic Word Embeddings Reveal Statistical Laws of Semantic Change](https://mlsanity.com/paper/1605.09096/) (2016)
- [Analysing Lexical Semantic Change with Contextualised Word Representations](https://mlsanity.com/paper/2004.14118/) (2020)
- [Survey of Computational Approaches to Lexical Semantic Change](https://mlsanity.com/paper/1811.06278/) (2018)
- [Dynamic Word Embeddings](https://mlsanity.com/paper/1702.08359/) (2017)
- [Efficient Second-Order TreeCRF for Neural Dependency Parsing](https://mlsanity.com/paper/2005.00975/) (2020)
- [Exploring Document-Level Literary Machine Translation with Parallel Paragraphs from World Literature](https://mlsanity.com/paper/2210.14250/) (2022)
- [Stack-Pointer Networks for Dependency Parsing](https://mlsanity.com/paper/1805.01087/) (2018)
- [Time-Out: Temporal Referencing for Robust Modeling of Lexical Semantic Change](https://mlsanity.com/paper/1906.01688/) (2019)
- [Training Temporal Word Embeddings with a Compass](https://mlsanity.com/paper/1906.02376/) (2019)
- [The optimality of syntactic dependency distances](https://mlsanity.com/paper/2007.15342/) (2020)
- [Anti dependency distance minimization in short sequences. A graph theoretic approach](https://mlsanity.com/paper/1906.05765/) (2019)
- [Diachronic Analysis of German Parliamentary Proceedings: Ideological Shifts through the Lens of Political Biases](https://mlsanity.com/paper/2108.06295/) (2021)
- [That's sick dude!: Automatic identification of word sense change across different timescales](https://mlsanity.com/paper/1405.4392/) (2014)
- [On the Linearity of Semantic Change: Investigating Meaning Variation via Dynamic Graph Models](https://mlsanity.com/paper/1704.02497/) (2017)
- [Graph-based Clustering for Detecting Semantic Change Across Time and Languages](https://mlsanity.com/paper/2402.01025/) (2024)
- [FastKASSIM: A Fast Tree Kernel-Based Syntactic Similarity Metric](https://mlsanity.com/paper/2203.08299/) (2022)
- [Cross-lingual Cross-temporal Summarization: Dataset, Models, Evaluation](https://mlsanity.com/paper/2306.12916/) (2023)
- [The scarcity of crossing dependencies: a direct outcome of a specific constraint?](https://mlsanity.com/paper/1601.03210/) (2016)
- [Natural Language Syntax Complies with the Free-Energy Principle](https://mlsanity.com/paper/2210.15098/) (2022)
- [Crossings as a side effect of dependency lengths](https://mlsanity.com/paper/1508.06451/) (2015)
- [Semantic Change and Emerging Tropes In a Large Corpus of New High German Poetry](https://mlsanity.com/paper/1909.12136/) (2019)

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

- [Model Misalignment and Language Change: Traces of AI-Associated Language in Unscripted Spoken English](https://mlsanity.com/paper/2508.00238/) (2025)
- [Exploring Diachronic and Diatopic Changes in Dialect Continua: Tasks, Datasets and Challenges](https://mlsanity.com/paper/2407.04010/) (2024)
- [Exploring Sound Change Over Time: A Review of Computational and Human Perception](https://mlsanity.com/paper/2407.05092/) (2024)

## Abstract (arXiv)

Many studies have shown that human languages tend to optimize for lower complexity and increased communication efficiency. Syntactic dependency distance, which measures the linear distance between dependent words, is often considered a key indicator of language processing difficulty and working memory load. The current paper looks at diachronic trends in syntactic language change in both English and German, using corpora of parliamentary debates from the last c. 160 years. We base our observations on five dependency parsers, including the widely used Stanford CoreNLP as well as 4 newer alternatives. Our analysis of syntactic language change goes beyond linear dependency distance and explores 15 metrics relevant to dependency distance minimization (DDM) and/or based on tree graph properties, such as the tree height and degree variance. Even though we have evidence that recent parsers trained on modern treebanks are not heavily affected by data 'noise' such as spelling changes and OCR errors in our historic data, we find that results of syntactic language change are sensitive to the parsers involved, which is a caution against using a single parser for evaluating syntactic language change as done in previous work. We also show that syntactic language change over the time period investigated is largely similar between English and German for the different metrics explored: only 4% of cases we examine yield opposite conclusions regarding upwards and downtrends of syntactic metrics across German and English. We also show that changes in syntactic measures seem to be more frequent at the tails of sentence length distributions. To our best knowledge, ours is the most comprehensive analysis of syntactic language change using modern NLP technology in recent corpora of English and German.