# Improved Neural Protoform Reconstruction via Reflex Prediction

arXiv: https://arxiv.org/abs/2403.18769 · HTML: https://mlsanity.com/paper/2403.18769/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 31 references that are in the map.
- First posted on arXiv 27 Mar 2024.

## Builds on (most cited first, 3 of 3)

- [Ab Antiquo: Neural Proto-language Reconstruction](https://mlsanity.com/paper/1908.02477/) (2019)
- [A New Framework for Fast Automated Phonological Reconstruction Using Trimmed Alignments and Sound Correspondence Patterns](https://mlsanity.com/paper/2204.04619/) (2022)
- [Jambu: A historical linguistic database for South Asian languages](https://mlsanity.com/paper/2306.02514/) (2023)

## Built on by (most cited first, 6 of 6)

- [Semisupervised Neural Proto-Language Reconstruction](https://mlsanity.com/paper/2406.05930/) (2024)
- [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)
- [Transformer-Enabled Diachronic Analysis of Vedic Sanskrit: Neural Methods for Quantifying Types of Language Change](https://mlsanity.com/paper/2512.05364/) (2025)
- [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)

Protolanguage reconstruction is central to historical linguistics. The comparative method, one of the most influential theoretical and methodological frameworks in the history of the language sciences, allows linguists to infer protoforms (reconstructed ancestral words) from their reflexes (related modern words) based on the assumption of regular sound change. Not surprisingly, numerous computational linguists have attempted to operationalize comparative reconstruction through various computational models, the most successful of which have been supervised encoder-decoder models, which treat the problem of predicting protoforms given sets of reflexes as a sequence-to-sequence problem. We argue that this framework ignores one of the most important aspects of the comparative method: not only should protoforms be inferable from cognate sets (sets of related reflexes) but the reflexes should also be inferable from the protoforms. Leveraging another line of research -- reflex prediction -- we propose a system in which candidate protoforms from a reconstruction model are reranked by a reflex prediction model. We show that this more complete implementation of the comparative method allows us to surpass state-of-the-art protoform reconstruction methods on three of four Chinese and Romance datasets.