# DHPLT: large-scale multilingual diachronic corpora and word representations for semantic change modelling

arXiv: https://arxiv.org/abs/2602.11968 · HTML: https://mlsanity.com/paper/2602.11968/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 29 references that are in the map.
- First posted on arXiv 12 Feb 2026.

## Builds on (most cited first, 21 of 21)

- [Unsupervised Cross-lingual Representation Learning at Scale](https://mlsanity.com/paper/1911.02116/) (2019)
- [Distributed Representations of Words and Phrases and their Compositionality](https://mlsanity.com/paper/1310.4546/) (2013)
- [Stanza: A Python Natural Language Processing Toolkit for Many Human Languages](https://mlsanity.com/paper/2003.07082/) (2020)
- [Beam Search Strategies for Neural Machine Translation](https://mlsanity.com/paper/1702.01806/) (2017)
- [Diachronic Word Embeddings Reveal Statistical Laws of Semantic Change](https://mlsanity.com/paper/1605.09096/) (2016)
- [SemEval-2020 Task 1: Unsupervised Lexical Semantic Change Detection](https://mlsanity.com/paper/2007.11464/) (2020)
- [Leveraging Contextual Embeddings for Detecting Diachronic Semantic Shift](https://mlsanity.com/paper/1912.01072/) (2019)
- [GPT or BERT: why not both?](https://mlsanity.com/paper/2410.24159/) (2024)
- [A Systematic Comparison of Contextualized Word Embeddings for Lexical Semantic Change](https://mlsanity.com/paper/2402.12011/) (2024)
- [Contextualized language models for semantic change detection: lessons learned](https://mlsanity.com/paper/2209.00154/) (2022)
- [Lexical Semantic Change Discovery](https://mlsanity.com/paper/2106.03111/) (2021)
- [Three-part diachronic semantic change dataset for Russian](https://mlsanity.com/paper/2106.08294/) (2021)
- [GASC: Genre-Aware Semantic Change for Ancient Greek](https://mlsanity.com/paper/1903.05587/) (2019)
- [Substitution-based Semantic Change Detection using Contextual Embeddings](https://mlsanity.com/paper/2309.02403/) (2023)
- [Do Not Fire the Linguist: Grammatical Profiles Help Language Models Detect Semantic Change](https://mlsanity.com/paper/2204.05717/) (2022)
- [CLASSLA-Stanza: The Next Step for Linguistic Processing of South Slavic Languages](https://mlsanity.com/paper/2308.04255/) (2023)
- [Analyzing Semantic Change through Lexical Replacements](https://mlsanity.com/paper/2404.18570/) (2024)
- [Definition generation for lexical semantic change detection](https://mlsanity.com/paper/2406.14167/) (2024)
- [Pretraining Language Models for Diachronic Linguistic Change Discovery](https://mlsanity.com/paper/2504.05523/) (2025)
- [SCDTour: Embedding Axis Ordering and Merging for Interpretable Semantic Change Detection](https://mlsanity.com/paper/2509.11818/) (2025)
- [Tracking Semantic Change in Slovene: A Novel Dataset and Optimal Transport-Based Distance](https://mlsanity.com/paper/2402.16596/) (2024)

## Built on by (most cited first, 1 of 1)

- [ChronoLens: Measuring Language Change Across Time, Languages, and Linguistic Levels](https://mlsanity.com/paper/2608.03507/) (2026)

## Abstract (arXiv)

In this resource paper, we present DHPLT, an open collection of diachronic corpora in 41 diverse languages. DHPLT is based on the web-crawled HPLT datasets; we use web crawl timestamps as the approximate signal of document creation time. The collection covers three time periods: 2011-2015, 2020-2021 and 2024-present (1 million documents per time period for each language). We additionally provide pre-computed word type and token embeddings and lexical substitutions for our chosen target words, while at the same time leaving it open for the other researchers to come up with their own target words using the same datasets. DHPLT aims at filling in the current lack of multilingual diachronic corpora for semantic change modelling (beyond a dozen of high-resource languages). It opens the way for a variety of new experimental setups in this field. All the resources described in this paper are available at https://data.hplt-project.org/three/diachronic/, sorted by language.