Same translation but different experience: the effects of highlighting on machine-translated conversations

Authors:
Ge Gao;Hao-Chuan Wang;Dan Cosley;Susan R. Fussell
Affiliations:
Cornell University, Ithaca, New York, USA;National Tsing Hua University, HsinChu, Taiwan;Cornell University, Ithaca, New York, USA;Cornell University, Ithaca, New York, USA
Venue:
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Year:
2013

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Abstract

Machine translation (MT) has the potential to allow members of multilingual organizations to interact via their own native languages, but issues with the quality of MT output have made it difficult to realize this potential. We hypothesized that highlighting keywords in MT output might make it easier for people to overlook translation errors and focus on what was intended by the message. To test this hypothesis, we conducted a laboratory experiment in which native English speakers interacted with a Mandarin-speaking confederate using machine translation. Participants performed three brainstorming tasks, under each of three conditions: no highlighting, keyword highlighting, and random highlighting. Our results indicated that people consider the identical messages clearer and less distracting when the keywords in the message are highlighted. Keyword highlighting also improved subjective impressions of the partner and the quality of the collaboration. These findings inform the design of future communication tools to support multilingual communications.