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

If one sees the place name Houston Mercer Dog Run in New York, how does one
know how to pronounce it? Assuming one knows that Houston in New York is
pronounced "how-ston" and not like the Texas city, then one can probably guess
that "how-ston" is also used in the name of the dog park. We present a novel
architecture that learns to use the pronunciations of neighboring names in
order to guess the pronunciation of a given target feature. Applied to Japanese
place names, we demonstrate the utility of the model to finding and proposing
corrections for errors in Google Maps.
To demonstrate the utility of this approach to structurally similar problems,
we also report on an application to a totally different task: Cognate reflex
prediction in comparative historical linguistics. A version of the code has
been open-sourced
(this https URL).

该研究提出了一种新的架构，利用相邻位置名称的发音来推测目标位置名称的发音，并应用于日本地名，展示了该模型在查找和纠错谷歌地图中的错误方面的实用性，并将其应用于比较历史语言学中的同源反射预测。

有用的邻居：利用邻居提高地理要素的发音

Helpful Neighbors: Leveraging Neighbors in Geographic Feature Pronunciation

This work describes an implementation of the "extended alignment" (or
"multitiers") approach for cognate reflex prediction, submitted to "Prediction
of Cognate Reflexes" shared task. Similarly to List2022d, the technique
involves an automatic extension of sequence alignments with multilayered
vectors that encode informational tiers on both site-specific traits, such as
sound classes and distinctive features, as well as contextual and
suprasegmental ones, conveyed by cross-site referrals and replication. The
method allows to generalize the problem of cognate reflex prediction as a
classification problem, with models trained using a parallel corpus of cognate
sets. A model using random forests is trained and evaluated on the shared task
for reflex prediction, and the experimental results are presented and discussed
along with some differences to other implementations.

本文提出了一种基于扩展对齐和多层向量的方法来预测同源词反应，并利用并行语料库进行模型的训练和评估，实验结果表明使用随机森林模型可以得到较好的预测效果。