Effect of Machine Translation in Interlingual Conversation: Lessons from a Formative Study


Kotaro Hara and Shamsi T. Iqbal


CHI '15: Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems, April 2015, Pages 3473–3482, https://doi.org/10.1145/2702123.2702407


Language barrier is the primary challenge for effective cross-lingual conversations. Spoken language translation (SLT) is perceived as a cost-effective alternative to less affordable human interpreters, but little research has been done on how people interact with such technology. Using a prototype translator application, we performed a formative evaluation to elicit how people interact with the technology and adapt their conversation style. We conducted two sets of studies with a total of 23 pairs (46 participants). Participants worked on storytelling tasks to simulate natural conversations with 3 different interface settings. Our findings show that collocutors naturally adapt their style of speech production and comprehension to compensate for inadequacies in SLT. We conclude the paper with the design guidelines that emerged from the analysis.