Phonological reconstruction is one of the central problems in historical
linguistics where a proto-word of an ancestral language is determined from the
observed cognate words of daughter languages. Computational approaches to
historical linguistics attempt to automate the task by learning models on
available linguistic data. Several ideas and techniques drawn from
computational biology have been successfully applied in the area of
computational historical linguistics. Following these lines, we adapt MSA
Transformer, a protein language model, to the problem of automated phonological
reconstruction. MSA Transformer trains on multiple sequence alignments as input
and is, thus, apt for application on aligned cognate words. We, hence, name our
model as Cognate Transformer. We also apply the model on another associated
task, namely, cognate reflex prediction, where a reflex word in a daughter
language is predicted based on cognate words from other daughter languages. We
show that our model outperforms the existing models on both tasks, especially
when it is pre-trained on masked word prediction task.

我们将蛋白质语言模型 MSA Transformer（多序列比对变换器）应用于自动语音演变重建问题，命名为同源变换器，并在同源反射预测任务中得出优于现有模型的结果，特别是在掩码词预测任务的预训练下。

自动音韵重构和同源语反射预测的认知变换器

Cognate Transformer for Automated Phonological Reconstruction and  Cognate Reflex Prediction

Computational approaches in historical linguistics have been increasingly
applied during the past decade and many new methods that implement parts of the
traditional comparative method have been proposed. Despite these increased
efforts, there are not many easy-to-use and fast approaches for the task of
phonological reconstruction. Here we present a new framework that combines
state-of-the-art techniques for automated sequence comparison with novel
techniques for phonetic alignment analysis and sound correspondence pattern
detection to allow for the supervised reconstruction of word forms in ancestral
languages. We test the method on a new dataset covering six groups from three
different language families. The results show that our method yields promising
results while at the same time being not only fast but also easy to apply and
expand.

本文介绍了一种新的计算机模型，该模型结合了最先进的自动化序列比对技术和新颖的语音对准分析技术以及声音对应模式检测技术，可用于监督祖先语言的单词形态学恢复，测试结果表明该方法既快速又易于实现和扩展。