论文标题
罗马尼亚变音符号恢复使用经常性神经网络
Romanian Diacritics Restoration Using Recurrent Neural Networks
论文作者
论文摘要
《变量恢复》是充分处理罗马尼亚文本而不是琐碎文本的强制性步骤，因为您通常需要上下文才能正确恢复角色。对罗马尼亚恢复的大多数以前的方法都不使用神经网络。在这样做的那些人中，没有针对这种特定语言专门优化的解决方案（即它们通常旨在使用许多不同的语言）。因此，我们提出了一种基于复发性神经网络的新型神经体系结构，可以在不同级别的抽象级别上参加信息以恢复变量。
Diacritics restoration is a mandatory step for adequately processing Romanian texts, and not a trivial one, as you generally need context in order to properly restore a character. Most previous methods which were experimented for Romanian restoration of diacritics do not use neural networks. Among those that do, there are no solutions specifically optimized for this particular language (i.e., they were generally designed to work on many different languages). Therefore we propose a novel neural architecture based on recurrent neural networks that can attend information at different levels of abstractions in order to restore diacritics.
