Year: 2024
Type: conference-paper
Abstract: Existing work implementing comparative reconstruction of ancestral languages (protolanguages) has usually required full supervision.However, historical reconstruction models are only of practical value if they can be trained with a limited amount of labeled data.We propose a semisupervised historical reconstruction task in which the model is trained on only a small amount of labeled data (cognate sets with proto-forms) and a large amount of unlabeled data (cognate sets without proto-forms).We propose a neural architecture for comparative reconstruction (DPD-BiReconstructor) incorporating an essential insight from linguists' comparative method: that reconstructed words should not only be reconstructable from their daughter words, but also deterministically transformable back into their daughter words.We show that this architecture is able to leverage unlabeled cognate sets to outperform strong semisupervised baselines on this novel task 1 .Vladimir I. Levenshtein.1966.Binary codes capable of correcting deletions, insertions, and reversals.In Soviet Physics Doklady, volume 10, pages 707-710.Soviet Union.
Authors: Liang Lu, Peirong Xie, David Mortensen
Institutions: Carnegie Mellon University, University of Southern California
Language: English
Subfield: Language and Linguistics
Field: Arts and Humanities
Domain: Social Sciences
Open Access status: hybrid
Funder: Carnegie Mellon University
OpenAlex ID: W4402671328