论文标题
通过深度学习模型了解Tieq越野
Understanding Tieq Viet with Deep Learning Models
论文作者
论文摘要
深度学习是恢复丢失的信息以及更难的逆功能计算问题的强大方法。当在自然语言处理中应用时，这种方法实质上是利用上下文来通过最大化来恢复信息的手段。不久前，一项名为TIEQ越野的语言研究在研究人员和社会中都是有争议的。我们发现这是一个很好的例子，可以证明深度学习模型恢复丢失的信息的能力。在TIEQ越野的提议中，标准越南人中的一些辅音被取代。本提案中写的句子可以解释为标准版本中的多个句子，其含义不同。我们要测试的假设是，如果我们将文本从越南语翻译成Tieq越南，深度学习模型是否可以恢复丢失的信息。
Deep learning is a powerful approach in recovering lost information as well as harder inverse function computation problems. When applied in natural language processing, this approach is essentially making use of context as a mean to recover information through likelihood maximization. Not long ago, a linguistic study called Tieq Viet was controversial among both researchers and society. We find this a great example to demonstrate the ability of deep learning models to recover lost information. In the proposal of Tieq Viet, some consonants in the standard Vietnamese are replaced. A sentence written in this proposal can be interpreted into multiple sentences in the standard version, with different meanings. The hypothesis that we want to test is whether a deep learning model can recover the lost information if we translate the text from Vietnamese to Tieq Viet.
