ChronoLens tracks language change across five parliaments
The framework uses frozen multilingual models to compare linguistic change from 1803 to 2026.
Why it matters
The work offers a method for studying language change across languages and linguistic levels using multilingual language models. It may help researchers compare how linguistic systems evolve without relying on incompatible representations.
The key points
- 1.ChronoLens analyzes 44.98 million parliamentary documents.
- 2.Sparse representations outperformed dense embeddings on linguistic-statistic alignment.
- 3.Languages differed in timing, magnitude and direction of change.
Researchers introduced ChronoLens, a framework for measuring historical language change across morphology, syntax, semantics and pragmatics in a shared analytical space. They applied it to 44.98 million documents and about 17.2 billion tokens from five parliamentary traditions spanning 1803 to 2026. Its sparse representations aligned more strongly with linguistic statistics than dense embeddings or a pooled sparse autoencoder, with ρ=0.72 versus 0.29 and 0.28.
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Read the paper before using multilingual embeddings to study historical language change.
Sources & original reporting
This brief summarizes and links to reporting from the publishers below.
- arXiv cs.AIChronoLens: Measuring Language Change Across Time, Languages, and Linguistic LevelsAug 5, 12:00 PM↗
- arXiv cs.CLChronoLens: Measuring Language Change Across Time, Languages, and Linguistic LevelsAug 5, 12:00 PM↗
- HF Daily PapersChronoLens: Measuring Language Change Across Time, Languages, and Linguistic LevelsAug 4, 4:00 AM↗
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