Understanding Tieq Viet with Deep Learning Models
This work addresses a specific linguistic challenge for NLP researchers, but it is incremental as it applies existing deep learning methods to a new dataset.
The authors tackled the problem of recovering lost information in text by testing whether deep learning models can reconstruct standard Vietnamese from the altered Tieq Viet version, where consonants are replaced, resulting in multiple interpretations.
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.