# Intent Detection for code-mix utterances in task oriented dialogue systems

arXiv: https://arxiv.org/abs/1812.02914 · HTML: https://mlsanity.com/paper/1812.02914/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Dec 2018.

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

- [Representativeness as a Forgotten Lesson for Multilingual and Code-switched Data Collection and Preparation](https://mlsanity.com/paper/2310.20470/) (2023)
- [Cutting Through the Noise: Boosting LLM Performance on Math Word Problems](https://mlsanity.com/paper/2406.15444/) (2024)
- [Social Intelligence Data Infrastructure: Structuring the Present and Navigating the Future](https://mlsanity.com/paper/2403.14659/) (2024)
- [IntentGPT: Few-shot Intent Discovery with Large Language Models](https://mlsanity.com/paper/2411.10670/) (2024)
- [Code Mixologist : A Practitioner's Guide to Building Code-Mixed LLMs](https://mlsanity.com/paper/2602.11181/) (2026)

## Abstract (arXiv)

Intent detection is an essential component of task oriented dialogue systems. Over the years, extensive research has been conducted resulting in many state of the art models directed towards resolving user's intents in dialogue. A variety of vector representations foruser utterances have been explored for the same. However, these models and vectorization approaches have more so been evaluated in a single language environment. Dialogude systems generally have to deal with queries in different languages. We thus conduct experiments across combinations of models and various vectors representations for Code Mix as well as multi language utterances and evaluate how these models scale to a multi language environment. Our aim is to find the best suitable combination of vector representation and models for the process of intent detection for Code Mix utterances. we have evaluated the experiments on two different datasets consisting of only Code Mix utterances and the other dataset consisting of English, Hindi and Code Mix English Hindi utterances.