mlsanityData to 7 Oct 2026

What talking you?: Translating Code-Mixed Messaging Texts to English

arXiv 2411.05253 ↗First posted 8 Nov 2024cs.CLcited by 4

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Abstract (arXiv)

Translation of code-mixed texts to formal English allow a wider audience to understand these code-mixed languages, and facilitate downstream analysis applications such as sentiment analysis. In this work, we look at translating Singlish, which is colloquial Singaporean English, to formal standard English. Singlish is formed through the code-mixing of multiple Asian languages and dialects. We analysed the presence of other Asian languages and variants which can facilitate translation. Our dataset is short message texts, written as informal communication between Singlish speakers. We use a multi-step prompting scheme on five Large Language Models (LLMs) for language detection and translation. Our analysis show that LLMs do not perform well in this task, and we describe the challenges involved in translation of code-mixed languages. We also release our dataset in this link https://github.com/luoqichan/singlish.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 19 of its 40 references that are in the map. First posted on arXiv 8 Nov 2024.

Builds on

19 of 40 references.

References · 19

  1. 2019BERTScore: Evaluating Text Generation with BERTcited by 5,462
  2. 2014Neural Machine Translation by Jointly Learning to Align and Translatecited by 4,530
  3. 2023Mistral 7Bcited by 3,065
  4. 2024Phi-3 Technical Report: A Highly Capable Language Model Locally on Your Phonecited by 1,883
  5. 2024Gemma: Open Models Based on Gemini Research and Technologycited by 889
  6. 2023YaRN: Efficient Context Window Extension of Large Language Modelscited by 584
  7. 2023Is ChatGPT A Good Translator? Yes With GPT-4 As The Enginecited by 172
  8. 2023SeaEval for Multilingual Foundation Models: From Cross-Lingual Alignment to Cultural Reasoningcited by 94
  9. 2024From LLM to NMT: Advancing Low-Resource Machine Translation with Claudecited by 57
  10. 2020A Survey of Deep Learning Techniques for Neural Machine Translationcited by 50
  11. 2020A Sentiment Analysis Dataset for Code-Mixed Malayalam-Englishcited by 50
  12. 2022L3Cube-HingCorpus and HingBERT: A Code Mixed Hindi-English Dataset and BERT Language Modelscited by 33
7 more
  1. 2018Sentiment Analysis of Code-Mixed Indian Languages: An Overview of SAIL_Code-Mixed Shared Task @ICON-2017cited by 23
  2. 2024Quality or Quantity? On Data Scale and Diversity in Adapting Large Language Models for Low-Resource Translationcited by 13
  3. 2021CodemixedNLP: An Extensible and Open NLP Toolkit for Code-Mixingcited by 8
  4. 2024Code-Mixer Ya Nahi: Novel Approaches to Measuring Multilingual LLMs' Code-Mixing Capabilitiescited by 7
  5. 2024Disentangling Singlish Discourse Particles with Task-Driven Representationcited by 5
  6. 2024Code-mixed Sentiment and Hate-speech Predictioncited by 4
  7. 2024IKUN for WMT24 General MT Task: LLMs Are here for Multilingual Machine Translationcited by 3

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 2; 2026: 2.

4 papers.

  1. 2025Lost in the Mix: Evaluating LLM Understanding of Code-Switched Textcited by 9
  2. 2025Toxicity-Aware Few-Shot Prompting for Low-Resource Singlish Translationcited by 1
  3. 2026Stylistic Evolution and LLM Neutrality in Singlish Languagecited by 0
  4. 2026The Hrunting of AI: Where and How to Improve English Dialectal Fairnesscited by 0