Computational historical linguistics seeks to systematically understand
processes of sound change, including during periods at which little to no
formal recording of language is attested. At the same time, few computational
resources exist which deeply explore phonological and morphological connections
between proto-languages and their descendants. This is particularly true for
the family of Italic languages. To assist historical linguists in the study of
Italic sound change, we introduce the Proto-Italic to Latin (PILA) dataset,
which consists of roughly 3,000 pairs of forms from Proto-Italic and Latin. We
provide a detailed description of how our dataset was created and organized.
Then, we exhibit PILA's value in two ways. First, we present baseline results
for PILA on a pair of traditional computational historical linguistics tasks.
Second, we demonstrate PILA's capability for enhancing other
historical-linguistic datasets through a dataset compatibility study.

计算历史语言学试图系统地理解声音变化过程，特别是在没有正式语言记录的时期。为了帮助了解伊特鲁里亚语系的音变，我们引入了 Proto-Italic to Latin (PILA) 数据集，其中包含大约 3000 个来自 Proto-Italic 和拉丁语的形式对。通过对数据集的详细描述，我们展示了 PILA 在计算历史语言学任务和增强其他历史语言学数据集方面的价值。

PILA：源于供意大利语和拉丁语的历史语言数据集

PILA: A Historical-Linguistic Dataset of Proto-Italic and Latin

While a great deal of work has been done on NLP approaches to lexical
semantic change detection, other aspects of language change have received less
attention from the NLP community. In this paper, we address the detection of
sound change through historical spelling. We propose that a sound change can be
captured by comparing the relative distance through time between their
distributions using PPMI character embeddings. We verify this hypothesis in
synthetic data and then test the method's ability to trace the well-known
historical change of lenition of plosives in Danish historical sources. We show
that the models are able to identify several of the changes under consideration
and to uncover meaningful contexts in which they appeared. The methodology has
the potential to contribute to the study of open questions such as the relative
chronology of sound shifts and their geographical distribution.

本文针对语言变化的另一方面 —— 声音变化进行了研究，提出了一种可以通过比较不同时期的分布距离来捕捉声音变化的理论，并通过 PPMI 字符嵌入比较并验证了这一理论，在丹麦历史文献中成功找到了若干个相关的语音变化。该方法可以用于探究一些未解决的问题。

历史声音变化建模：通过历时字符嵌入 Letters From the Past

Letters From the Past: Modeling Historical Sound Change Through Diachronic Character Embeddings

Historical linguists have identified regularities in the process of historic
sound change. The comparative method utilizes those regularities to reconstruct
proto-words based on observed forms in daughter languages. Can this process be
efficiently automated? We address the task of proto-word reconstruction, in
which the model is exposed to cognates in contemporary daughter languages, and
has to predict the proto word in the ancestor language. We provide a novel
dataset for this task, encompassing over 8,000 comparative entries, and show
that neural sequence models outperform conventional methods applied to this
task so far. Error analysis reveals variability in the ability of neural model
to capture different phonological changes, correlating with the complexity of
the changes. Analysis of learned embeddings reveals the models learn
phonologically meaningful generalizations, corresponding to well-attested
phonological shifts documented by historical linguistics.

本文研究历史语言学中的声音变化规律及该规律如何使用比较法进行原形词的重构，提出使用神经序列模型对比较法数据集中的超过 8000 个比较条目进行原形词的预测，结果表明神经序列模型优于现有的传统方法。语音学变化的复杂性存在一定的差异，但该模型仍学习了有意义的语音规律。