# A Dataset for Building Code-Mixed Goal Oriented Conversation Systems

arXiv: https://arxiv.org/abs/1806.05997 · HTML: https://mlsanity.com/paper/1806.05997/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Jun 2018.

## Built on by (most cited first, 16 of 16)

- [ASCEND: A Spontaneous Chinese-English Dataset for Code-switching in Multi-turn Conversation](https://mlsanity.com/paper/2112.06223/) (2021)
- [The Decades Progress on Code-Switching Research in NLP: A Systematic Survey on Trends and Challenges](https://mlsanity.com/paper/2212.09660/) (2022)
- [HinGE: A Dataset for Generation and Evaluation of Code-Mixed Hinglish Text](https://mlsanity.com/paper/2107.03760/) (2021)
- [ArzEn-ST: A Three-way Speech Translation Corpus for Code-Switched Egyptian Arabic - English](https://mlsanity.com/paper/2211.12000/) (2022)
- [Code-switched inspired losses for generic spoken dialog representations](https://mlsanity.com/paper/2108.12465/) (2021)
- [MulZDG: Multilingual Code-Switching Framework for Zero-shot Dialogue Generation](https://mlsanity.com/paper/2208.08629/) (2022)
- [Exploring Methods for Building Dialects-Mandarin Code-Mixing Corpora: A Case Study in Taiwanese Hokkien](https://mlsanity.com/paper/2301.08937/) (2023)
- [Constructing Code-mixed Universal Dependency Forest for Unbiased Cross-lingual Relation Extraction](https://mlsanity.com/paper/2305.12258/) (2023)
- [GupShup: An Annotated Corpus for Abstractive Summarization of Open-Domain Code-Switched Conversations](https://mlsanity.com/paper/2104.08578/) (2021)
- [IndoRobusta: Towards Robustness Against Diverse Code-Mixed Indonesian Local Languages](https://mlsanity.com/paper/2311.12405/) (2023)
- [The Effectiveness of Intermediate-Task Training for Code-Switched Natural Language Understanding](https://mlsanity.com/paper/2107.09931/) (2021)
- [A Comprehensive Understanding of Code-mixed Language Semantics using Hierarchical Transformer](https://mlsanity.com/paper/2204.12753/) (2022)
- [Automatic Identification of Motivation for Code-Switching in Speech Transcripts](https://mlsanity.com/paper/2212.08565/) (2022)
- [IruMozhi: Automatically classifying diglossia in Tamil](https://mlsanity.com/paper/2311.07804/) (2023)
- [Harmonizing Code-mixed Conversations: Personality-assisted Code-mixed Response Generation in Dialogues](https://mlsanity.com/paper/2401.12995/) (2024)
- [IndicTalk: A Large-Scale Persona-Based Multilingual Conversational Corpus for Indic Languages](https://mlsanity.com/paper/2607.23242/) (2026)

## Abstract (arXiv)

There is an increasing demand for goal-oriented conversation systems which can assist users in various day-to-day activities such as booking tickets, restaurant reservations, shopping, etc. Most of the existing datasets for building such conversation systems focus on monolingual conversations and there is hardly any work on multilingual and/or code-mixed conversations. Such datasets and systems thus do not cater to the multilingual regions of the world, such as India, where it is very common for people to speak more than one language and seamlessly switch between them resulting in code-mixed conversations. For example, a Hindi speaking user looking to book a restaurant would typically ask, "Kya tum is restaurant mein ek table book karne mein meri help karoge?" ("Can you help me in booking a table at this restaurant?"). To facilitate the development of such code-mixed conversation models, we build a goal-oriented dialog dataset containing code-mixed conversations. Specifically, we take the text from the DSTC2 restaurant reservation dataset and create code-mixed versions of it in Hindi-English, Bengali-English, Gujarati-English and Tamil-English. We also establish initial baselines on this dataset using existing state of the art models. This dataset along with our baseline implementations is made publicly available for research purposes.