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It is well-known that multilingual users mix languages while interacting with others, as well as in their interactions with computer systems (such as query formulation in text-\/voice-based search interfaces and digital assistants). Linguists refer to this phenomenon as code-mixing or code-switching. Do multilingual users also prefer chatbots that can respond in a code-mixed language over those which cannot? In order to inform the design of chatbots for multilingual users, we conduct a mixed-method user-study (N=91) where we examine how conversational agents, that code-mix and reciprocate the users' mixing choices over multiple conversation turns, are evaluated and perceived by bilingual users. We design a human-in-the-loop chatbot with two different code-mixing policies -- (a) always code-mix irrespective of user behavior, and (b) nudge with subtle code-mixed cues and reciprocate only if the user, in turn, code-mixes. These two are contrasted with a monolingual chatbot that never code-mixed. Users are asked to interact with the bots, and provide ratings on perceived naturalness and personal preference. They are also asked open-ended questions around what they (dis)liked about the bots. Analysis of the chat logs, users' ratings, and qualitative responses reveal that multilingual users strongly prefer chatbots that can code-mix. We find that self-reported language proficiency is the strongest predictor of user preferences. Compared to the Always code-mix policy, Nudging emerges as a low-risk low-gain policy which is equally acceptable to all users. Nudging as a policy is further supported by the observation that users who rate the code-mixing bot higher typically tend to reciprocate the language mixing pattern of the bot. These findings present a first step towards developing conversational systems that are more human-like and engaging by virtue of adapting to the users' linguistic style.<\/jats:p>","DOI":"10.1145\/3392846","type":"journal-article","created":{"date-parts":[[2020,5,29]],"date-time":"2020-05-29T16:01:06Z","timestamp":1590768066000},"page":"1-23","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":18,"title":["Do Multilingual Users Prefer Chat-bots that Code-mix? Let's Nudge and Find Out!"],"prefix":"10.1145","volume":"4","author":[{"given":"Anshul","family":"Bawa","sequence":"first","affiliation":[{"name":"Microsoft Research, Bengaluru, Karnataka, India"}]},{"given":"Pranav","family":"Khadpe","sequence":"additional","affiliation":[{"name":"Microsoft Research, Bangalore, Karnataka, India"}]},{"given":"Pratik","family":"Joshi","sequence":"additional","affiliation":[{"name":"Microsoft Research, Bangalore, Karnataka, India"}]},{"given":"Kalika","family":"Bali","sequence":"additional","affiliation":[{"name":"Microsoft Research, Bangalore, Karnataka, India"}]},{"given":"Monojit","family":"Choudhury","sequence":"additional","affiliation":[{"name":"Microsoft Research, Bangalore, Karnataka, India"}]}],"member":"320","published-online":{"date-parts":[[2020,5,29]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jneuroling.2008.02.001"},{"key":"e_1_2_1_2_1","volume-title":"The pragmatics of code-switching: A sequential approach. 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