研究人机对话中混用西语和英语的策略，发现规律性切换更受用户欢迎。
Strategies of Code-switching in Human-Machine Dialogs
- 设计可按不同策略混用西英的语言聊天机器人
- 规则化切换提升用户体验与任务完成率，随机或语法错误则适得其反
- 为多语言交互研究提供新方法，适合语言技术与人机交互研究者
多数人是多语者，且会进行语言切换，但混用语言的特征尚未完全明晰。我们开发了一款聊天机器人，能与人类参与者在地图任务中使用西班牙语和英语混合交流。通过两次实验，测试了机器人按不同策略进行语言切换的可行性，并考察参与者是否对话语和语法模式的变化敏感。结果显示，只要切换行为可预测，参与者普遍喜欢与机器人进行语言切换；当切换随机或语法不合法（如出现未被接受的混合名词短语，如'la fork'）时，参与者体验变差，任务完成度也下降。结果表明，若多语言技术开发不足，可能带来负面影响，但也展示了该技术在研究双语语言使用方面的潜力。
原文摘要 · Abstract (English)
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.
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