Transformed Protoform Reconstruction
Young Min Kim, Kalvin Chang, Chenxuan Cui, David R. Mortensen
Abstract
Protoform reconstruction is the task of inferring what morphemes or words appeared like in the ancestral languages of a set of daughter languages. Meloni et al (2021) achieved the state-of-the-art on Latin protoform reconstruction with an RNN-based encoder-decoder with attention model. We update their model with the state-of-the-art seq2seq model: the Transformer. Our model outperforms their model on a suite of different metrics on two different datasets: their Romance data of 8,000 cognates spanning 5 languages and a Chinese dataset (Hou 2004) of 800+ cognates spanning 39 varieties. We also probe our model for potential phylogenetic signal contained in the model. Our code is publicly available at https://github.com/cmu-llab/acl-2023.
BibTeX
@inproceedings{kim-etal-2023-transformed,
title = "Transformed Protoform Reconstruction",
author = "Kim, Young Min and
Chang, Kalvin and
Cui, Chenxuan and
Mortensen, David R.",
editor = "Rogers, Anna and
Boyd-Graber, Jordan and
Okazaki, Naoaki",
booktitle = "Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
month = jul,
year = "2023",
address = "Toronto, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.acl-short.3/",
doi = "10.18653/v1/2023.acl-short.3",
pages = "24--38"
}