# Marathi-English Code-mixed Text Generation

arXiv: https://arxiv.org/abs/2309.16202 · HTML: https://mlsanity.com/paper/2309.16202/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 19 references that are in the map.
- First posted on arXiv 28 Sep 2023.

## Builds on (most cited first, 8 of 8)

- [Stanza: A Python Natural Language Processing Toolkit for Many Human Languages](https://mlsanity.com/paper/2003.07082/) (2020)
- [Word Alignment by Fine-tuning Embeddings on Parallel Corpora](https://mlsanity.com/paper/2101.08231/) (2021)
- [Prompting Multilingual Large Language Models to Generate Code-Mixed Texts: The Case of South East Asian Languages](https://mlsanity.com/paper/2303.13592/) (2023)
- [L3Cube-HingCorpus and HingBERT: A Code Mixed Hindi-English Dataset and BERT Language Models](https://mlsanity.com/paper/2204.08398/) (2022)
- [A New Dataset for Natural Language Inference from Code-mixed Conversations](https://mlsanity.com/paper/2004.05051/) (2020)
- [HinGE: A Dataset for Generation and Evaluation of Code-Mixed Hinglish Text](https://mlsanity.com/paper/2107.03760/) (2021)
- [Exploring Text-to-Text Transformers for English to Hinglish Machine Translation with Synthetic Code-Mixing](https://mlsanity.com/paper/2105.08807/) (2021)
- [Towards Developing a Multilingual and Code-Mixed Visual Question Answering System by Knowledge Distillation](https://mlsanity.com/paper/2109.04653/) (2021)

## Built on by (most cited first, 2 of 2)

- [Multilingual Large Language Model: A Survey of Resources, Taxonomy and Frontiers](https://mlsanity.com/paper/2404.04925/) (2024)
- [IndicTriMix: Developing Language Identification Datasets and Models for Tri-Language Code-Mixing](https://mlsanity.com/paper/2609.11851/) (2026)

## Abstract (arXiv)

Code-mixing, the blending of linguistic elements from distinct languages to form meaningful sentences, is common in multilingual settings, yielding hybrid languages like Hinglish and Minglish. Marathi, India's third most spoken language, often integrates English for precision and formality. Developing code-mixed language systems, like Marathi-English (Minglish), faces resource constraints. This research introduces a Marathi-English code-mixed text generation algorithm, assessed with Code Mixing Index (CMI) and Degree of Code Mixing (DCM) metrics. Across 2987 code-mixed questions, it achieved an average CMI of 0.2 and an average DCM of 7.4, indicating effective and comprehensible code-mixed sentences. These results offer potential for enhanced NLP tools, bridging linguistic gaps in multilingual societies.