人机对话中的语码转换策略
Strategies of Code-switching in Human-Machine Dialogs
AI总结：
本研究开发了一个能使用西班牙语和英语进行语码转换的聊天机器人来完成 Map Task，通过两项实验考察了此类实验研究双语使用的可行性及参与者对语篇和语法模式变化的敏感性，发现可预测的语码转换能提升参与者体验与成功率。
摘要：
大多数人都是多语者，且大多数多语者会进行语码转换，然而语码转换语言的特性尚未被完全理解。我们开发了一个聊天机器人，能够使用语码转换的西班牙语和英语与人类参与者共同完成 Map Task。在两项实验中，我们促使该机器人根据不同策略进行语码转换，考察了（1）此类实验用于研究双语语言使用的可行性，以及（2）参与者是否对语篇和语法模式的变化敏感。只要机器人产生可预测的语码转换行为，参与者通常喜欢与我们的机器人进行语码转换；当语码转换是随机的或不合乎语法时（例如产生未经证实的、不一致的混合语言名词短语，如“la fork”），参与者对该任务的喜爱程度降低，完成任务的成功率也较低。这些结果强调了部署不够成熟的多语言语言技术的潜在弊端，同时也展示了此类技术在开展双语语言使用研究方面的前景。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
