mlsanityData to 14 Oct 2026

Strategies of Code-switching in Human-Machine Dialogs

arXiv 2508.07325 ↗First posted 10 Aug 2025cs.CL cs.AIcited by 0

Not on the map. Why?

Abstract (arXiv)

Most people are multilingual, and most multilinguals code-switch, yet the characteristics of code-switched language are not fully understood. We developed a chatbot capable of completing a Map Task with human participants using code-switched Spanish and English. In two experiments, we prompted the bot to code-switch according to different strategies, examining (1) the feasibility of such experiments for investigating bilingual language use, and (2) whether participants would be sensitive to variations in discourse and grammatical patterns. Participants generally enjoyed code-switching with our bot as long as it produced predictable code-switching behavior; when code-switching was random or ungrammatical (as when producing unattested incongruent mixed-language noun phrases, such as `la fork'), participants enjoyed the task less and were less successful at completing it. These results underscore the potential downsides of deploying insufficiently developed multilingual language technology, while also illustrating the promise of such technology for conducting research on bilingual language use.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 4 of its 59 references that are in the map. First posted on arXiv 10 Aug 2025.

Builds on

4 of 59 references.

References · 4

  1. 2017Attention Is All You Needcited by 60,361
  2. 2023GPT-4 Technical Reportcited by 18,396
  3. 2018Code-switching Sentence Generation by Generative Adversarial Networks and its Application to Data Augmentationcited by 26
  4. 2019A Deep Generative Model for Code-Switched Textcited by 13

Built on by

Not cited yet.