  A sentence is typically treated as the minimal syntactic unit used for
extracting valuable information from a longer piece of text. However, in
written Thai, there are no explicit sentence markers. We proposed a deep
learning model for the task of sentence segmentation that includes three main
contributions. First, we integrate n-gram embedding as a local representation
to capture word groups near sentence boundaries. Second, to focus on the
keywords of dependent clauses, we combine the model with a distant
representation obtained from self-attention modules. Finally, due to the
scarcity of labeled data, for which annotation is difficult and time-consuming,
we also investigate and adapt Cross-View Training (CVT) as a semi-supervised
learning technique, allowing us to utilize unlabeled data to improve the model
representations. In the Thai sentence segmentation experiments, our model
reduced the relative error by 7.4% and 10.5% compared with the baseline models
on the Orchid and UGWC datasets, respectively. We also applied our model to the
task of pronunciation recovery on the IWSLT English dataset. Our model
outperformed the prior sequence tagging models, achieving a relative error
reduction of 2.5%. Ablation studies revealed that utilizing n-gram
presentations was the main contributing factor for Thai, while the
semi-supervised training helped the most for English.
