A Human-Centered Approach to Trustworthy Machine Translation

Machine Translation (MT) tools are more accessible than ever, but a key challenge remains: how can we help users rely on them appropriately, especially when they don't understand one of the languages involved? This talk will describe how my group has addressed this problem in recent years.
I'll begin by sharing the results of user studies designed to understand how people build trust in and reliance on MT. These findings led us to reframe MT evaluation to provide actionable feedback that helps people assess the impact of translation errors. To achieve this, we introduce techniques based on large language models to ask and answer questions about a translated text, and to detect errors in speech translations with minimal supervision.
Throughout the talk, I will highlight open questions and research directions for designing language technology that genuinely supports people in communicating across language barriers.