论文标题
轻量级变量恢复的卷积神经网络扩张
Dilated Convolutional Neural Networks for Lightweight Diacritics Restoration
论文作者
论文摘要
在基于拉丁语的英语主导的互联网语言环境中，变量恢复已成为无处不在的任务。在本文中，我们描述了一个小的足迹1D扩张的基于卷积的方法，该方法在角色级别上运行。我们发现，基于一维扩张的卷积神经网络的解决方案是基于递归神经网络或用于变量恢复任务的语言建模的模型的竞争替代方案。我们的解决方案超过了类似尺寸的模型的性能，并且在较大的模型中也具有竞争力。我们解决方案的一个特殊功能是它甚至在Web浏览器中本地运行。我们还提供了基于浏览器的实现的工作示例。我们的模型对不同的语料库进行了评估，重点是匈牙利语言。我们进行了有关模型与三个匈牙利语料库相关的概括能力的比较测量。我们还分析了错误的错误，以了解基于语料库的自我监督培训的局限性。
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.
