# Ab Antiquo: Neural Proto-language Reconstruction

arXiv: https://arxiv.org/abs/1908.02477 · HTML: https://mlsanity.com/paper/1908.02477/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Aug 2019.

## Built on by (most cited first, 18 of 18)

- [A New Framework for Fast Automated Phonological Reconstruction Using Trimmed Alignments and Sound Correspondence Patterns](https://mlsanity.com/paper/2204.04619/) (2022)
- [Computational historical linguistics and language diversity in South Asia](https://mlsanity.com/paper/2203.12524/) (2022)
- [Improved Neural Protoform Reconstruction via Reflex Prediction](https://mlsanity.com/paper/2403.18769/) (2024)
- [Neural Unsupervised Reconstruction of Protolanguage Word Forms](https://mlsanity.com/paper/2211.08684/) (2022)
- [Cognate Transformer for Automated Phonological Reconstruction and Cognate Reflex Prediction](https://mlsanity.com/paper/2310.07487/) (2023)
- [Generating Feature Vectors from Phonetic Transcriptions in Cross-Linguistic Data Formats](https://mlsanity.com/paper/2405.04271/) (2024)
- [Jambu: A historical linguistic database for South Asian languages](https://mlsanity.com/paper/2306.02514/) (2023)
- [Semisupervised Neural Proto-Language Reconstruction](https://mlsanity.com/paper/2406.05930/) (2024)
- [Transformed Protoform Reconstruction](https://mlsanity.com/paper/2307.01896/) (2023)
- [Can Large Language Models Code Like a Linguist?: A Case Study in Low Resource Sound Law Induction](https://mlsanity.com/paper/2406.12725/) (2024)
- [PBEBench: A Multi-Step Programming by Examples Reasoning Benchmark inspired by Historical Linguistics](https://mlsanity.com/paper/2505.23126/) (2025)
- [Representing and Computing Uncertainty in Phonological Reconstruction](https://mlsanity.com/paper/2310.12727/) (2023)
- [Approaching Reflex Predictions as a Classification Problem Using Extended Phonological Alignments](https://mlsanity.com/paper/2205.09570/) (2022)
- [Multi-Dialectal Representation Learning of Sinitic Phonology](https://mlsanity.com/paper/2307.01209/) (2023)
- [Neural Proto-Language Reconstruction](https://mlsanity.com/paper/2404.15690/) (2024)
- [PILA: A Historical-Linguistic Dataset of Proto-Italic and Latin](https://mlsanity.com/paper/2404.16341/) (2024)
- [Phonetic Reconstruction of the Consonant System of Middle Chinese via Mixed Integer Optimization](https://mlsanity.com/paper/2502.04625/) (2025)
- [Unsupervised Protoform Reconstruction through Parsimonious Rule-guided Heuristics and Evolutionary Search](https://mlsanity.com/paper/2506.10614/) (2025)

## Abstract (arXiv)

Historical linguists have identified regularities in the process of historic sound change. The comparative method utilizes those regularities to reconstruct proto-words based on observed forms in daughter languages. Can this process be efficiently automated? We address the task of proto-word reconstruction, in which the model is exposed to cognates in contemporary daughter languages, and has to predict the proto word in the ancestor language. We provide a novel dataset for this task, encompassing over 8,000 comparative entries, and show that neural sequence models outperform conventional methods applied to this task so far. Error analysis reveals variability in the ability of neural model to capture different phonological changes, correlating with the complexity of the changes. Analysis of learned embeddings reveals the models learn phonologically meaningful generalizations, corresponding to well-attested phonological shifts documented by historical linguistics.