Language Detection for Transliterated Content

architectures
production
social-sciences
hci
Internet transcends barriers, transliteration challenges addressed using BERT and Google Translate API.
Author

Selva Kumar S, Afifah Khan Mohammed Ajmal Khan, Chirag Manjeshwar, Imadh Ajaz Banday

Published

January 9, 2024

Summary:

The article discusses the challenges of accurately detecting the source language of transliterated text, particularly in the context of digital communication. The authors address this challenge by utilizing a dataset of phone text messages in Hindi and Russian transliterated into English, and employing BERT for language classification and Google Translate API for transliteration conversion. The research demonstrates the exceptional proficiency of their model in accurately identifying and classifying languages from transliterated text, with a validation accuracy of 99%. The study emphasizes the pivotal role of comprehensive datasets for training Large Language Models (LLMs) like BERT and holds promise for applications in content moderation, analytics, and fostering a globally connected community engaged in meaningful dialogue.

Major Findings:

  1. The research pioneers innovative approaches to identify and convert transliterated text, navigating challenges in the diverse linguistic landscape of digital communication.
  2. The model showcases exceptional proficiency in accurately identifying and classifying languages from transliterated text, with a validation accuracy of 99%.
  3. The study holds promise for applications in content moderation, analytics, and fostering a globally connected community engaged in meaningful dialogue.

Analysis and Critique:

The article effectively addresses the challenges of language detection for transliterated content and presents a robust model with a high validation accuracy. However, the study could benefit from a more detailed discussion of potential limitations, such as the generalizability of the model to other languages and the impact of transliteration variations. Additionally, further research on the practical applications and real-world implications of the model’s findings would enhance the overall contribution of the study.

Appendix

Model gpt-3.5-turbo-1106
Date Generated 2024-02-26
Abstract https://arxiv.org/abs/2401.04619v1
HTML https://browse.arxiv.org/html/2401.04619v1
Truncated False
Word Count 3561