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.
翻译:深层学习是恢复丢失信息的有力方法,也是更强硬的反函数计算问题。当应用于自然语言处理时,这一方法基本上是利用上下文作为手段,通过可能性最大化来恢复信息。不久前,一个名为“铁越”的语言研究在研究人员和社会上都引起争议。我们发现,这是表明深层学习模型恢复丢失信息能力的一个很好的例子。在铁越的建议中,标准越南的一些对应词被取代。本提案中写成的一句话可以被解释为标准版本中的多个句子,具有不同的含义。我们要测试的假设是,如果我们将文字从越南翻译到铁越,那么深层学习模型能否恢复丢失的信息。