论文标题
使用经常性神经网络恢复零碎的巴比伦文本
Restoration of Fragmentary Babylonian Texts Using Recurrent Neural Networks
论文作者
论文摘要
有关古代美索不达米亚历史和文化的主要信息来源是粘土楔形片。尽管是一项宝贵的资源，但许多平板电脑都被分散，导致缺少信息。目前，这些缺失的零件是由专家手动完成的。在这项工作中，我们调查了协助学者，甚至可以自动完成古代阿卡梅尼德时期巴比伦尼亚文本中的休息时间，并使用经常性的神经网络对语言进行建模。
The main source of information regarding ancient Mesopotamian history and culture are clay cuneiform tablets. Despite being an invaluable resource, many tablets are fragmented leading to missing information. Currently these missing parts are manually completed by experts. In this work we investigate the possibility of assisting scholars and even automatically completing the breaks in ancient Akkadian texts from Achaemenid period Babylonia by modelling the language using recurrent neural networks.
