Diacritics restoration has become a ubiquitous task in the
Latin-alphabet-based English-dominated Internet language environment. In this
paper, we describe a small footprint 1D dilated convolution-based approach
which operates on a character-level. We find that solutions based on 1D dilated
convolutional neural networks are competitive alternatives to models based on
recursive neural networks or linguistic modeling for the task of diacritics
restoration. Our solution surpasses the performance of similarly sized models
and is also competitive with larger models. A special feature of our solution
is that it even runs locally in a web browser. We also provide a working
example of this browser-based implementation. Our model is evaluated on
different corpora, with emphasis on the Hungarian language. We performed
comparative measurements about the generalization power of the model in
relation to three Hungarian corpora. We also analyzed the errors to understand
the limitation of corpus-based self-supervised training.

本研究提出了一种基于一维膨胀卷积的字符级别方法，用于重建拉丁字母为基础、英语为主导的互联网语言环境中的发音符号。该方法比基于递归神经网络或语言建模的方法表现更佳，且适用于 Web 浏览器，尤其适用于匈牙利语等语言的应用。