Deep Learning Based Vietnamese Diacritics Restoration Diacritics are very important in diacritical languages, because the meaning of sentences can be changed in accordance to diacritics. Writing without diacritics makes the sentences ambiguous; however, there are several reasons make people do not write words with diacritics, such as fast typing, convenience, or texting on unsupported diacritics devices. As a result, these texts are very difficult to process on further natural language processing (NLP) tasks like machine translation, sentiment analysis, or question answering system. Therefore, diacritics restoration is critical for further usage or processing in NLP related tasks. In this study, we propose a method which combines convolutional neural network (CNN) and bidirectional gated recurrent unit (BiGRU) to restore diacritics. In addition, we use residual block to resolve vanishing gradient problem of recurrent neural networks. We applied the model for restoring diacritics of Vietnamese language that has the highest ratio of diacritics in words. This approach has character accuracy at 98.63% and word accuracy at 94.77%.

On the Ability of a CNN to Realize ImagetoImage Language Conversion The purpose of this paper is to reveal the ability that Convolutional Neural Networks (CNN) have on the novel task of imagetoimage language conversion. We propose a new network to tackle this task by converting images of Korean Hangul characters directly into images of the phonetic Latin character equivalent. The conversion rules between Hangul and the phonetic symbols are not explicitly provided. The results of the proposed network show that it is possible to perform imagetoimage language conversion. Moreover, it shows that it can grasp the structural features of Hangul even from limited learning data. In addition, it introduces a new network to use when the input and output have significantly different features.