Understanding Tieq Viet with Deep Learning Models

TL;DR

This paper explores the capability of deep learning models to recover original Vietnamese text from Tieq Viet, a modified version with consonant replacements, demonstrating the potential of neural networks in linguistic information restoration.

Contribution

The study applies deep learning to recover original Vietnamese from Tieq Viet, showcasing a novel application of neural models in handling linguistically altered text.

Findings

Deep learning models can effectively recover original Vietnamese from Tieq Viet.

The approach demonstrates potential for linguistic restoration tasks.

Results indicate high accuracy in recovering lost information.

Abstract

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.