# Normalization of Transliterated Words in Code-Mixed Data Using Seq2Seq Model & Levenshtein Distance

arXiv: https://arxiv.org/abs/1805.08701 · HTML: https://mlsanity.com/paper/1805.08701/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 May 2018.

## Built on by (most cited first, 5 of 5)

- [The Decades Progress on Code-Switching Research in NLP: A Systematic Survey on Trends and Challenges](https://mlsanity.com/paper/2212.09660/) (2022)
- [Sequence-to-Sequence Lexical Normalization with Multilingual Transformers](https://mlsanity.com/paper/2110.02869/) (2021)
- [Lemmatization of Historical Old Literary Finnish Texts in Modern Orthography](https://mlsanity.com/paper/2107.03266/) (2021)
- [Script Normalization for Unconventional Writing of Under-Resourced Languages in Bilingual Communities](https://mlsanity.com/paper/2305.16407/) (2023)
- [Pre-Editorial Normalization for Automatically Transcribed Medieval Manuscripts in Old French and Latin](https://mlsanity.com/paper/2602.13905/) (2026)

## Abstract (arXiv)

Building tools for code-mixed data is rapidly gaining popularity in the NLP research community as such data is exponentially rising on social media. Working with code-mixed data contains several challenges, especially due to grammatical inconsistencies and spelling variations in addition to all the previous known challenges for social media scenarios. In this article, we present a novel architecture focusing on normalizing phonetic typing variations, which is commonly seen in code-mixed data. One of the main features of our architecture is that in addition to normalizing, it can also be utilized for back-transliteration and word identification in some cases. Our model achieved an accuracy of 90.27% on the test data.