Understanding and Being Understood: User Strategies for Identifying and Recovering From Mistranslations in Machine Translation-Mediated Chat


Machine translation (MT) is now widely and freely available, and has the potential to greatly improve cross-lingual communication. In order to use MT reliably and safely, end users must be able to assess the quality of system outputs and determine how much they can rely on them to guide their decisions and actions. However, it can be difficult for users to detect and recover from mistranslations due to limited language skills. In this work we collected 19 MT-mediated role-play conversations in housing and employment scenarios, and conducted in-depth interviews to understand how users identify and recover from translation errors. Participants communicated using four language pairs: English, and one of Spanish, Farsi, Igbo, or Tagalog. We conducted qualitative analysis to understand user challenges in light of limited system transparency, strategies for recovery, and the kinds of translation errors that proved more or less difficult for users to overcome. We found that users broadly lacked relevant and helpful information to guide their assessments of translation quality. Instances where a user erroneously thought they had understood a translation correctly were rare but held the potential for serious consequences in the real world. Finally, inaccurate and disfluent translations had social consequences for participants, because it was difficult to discern when a disfluent message was reflective of the other person’s intentions, or an artifact of imperfect MT. We draw on theories of grounding and repair in communication to contextualize these findings, and propose design implications for explainable AI (XAI) researchers, MT researchers, as well as collaboration among them to support transparency and explainability in MT. These directions include handling typos and non-standard grammar common in interpersonal communication, making MT in interfaces more visible to help users evaluate errors, supporting collaborative repair of conversation breakdowns, and communicating model strengths and weaknesses to users.

ACM Conference on Fairness, Accountability, and Transparency 2022