The Problem

Language barriers continue to create significant challenges for travelers, immigrants, frontline workers, and international professionals, leading to confusion, delays, and miscommunication in both personal and professional contexts.

Current translation solutions are often too slow for real-time interactions, struggle with regional dialects, and fail to handle idioms or cultural nuances. They also rely heavily on constant internet access and require users to manually operate their devices mid-conversation, disrupting the natural flow of communication.

In high-stakes or fast-paced situations such as transit announcements, public service interactions, or emergency scenarios—these shortcomings can escalate from mild inconvenience to critical communication failures.

Problem Characteristics

Real-world failures of mainstream apps: Joanna Graphman, a CS 410 student, recalled using Google Translate in Japan and experiencing mistranslations and delays that frustrated locals and made interactions awkward.
Emergency communication gaps: Caleb Tucker, a project team member, described a situation at a zoo where a lost child spoke no English. The lack of immediate, effective translation made it harder to reunite her with her parents.
Cultural nuance gaps in machine translation: Transformer-based models can translate quickly but often miss context-dependent meanings. For example, the phrase “File that in the bin” may be correctly translated literally but misunderstood depending on cultural context. “Bin” can mean “folder” in some U.S. workplaces or “trash” in the U.K., leading to potentially serious errors.
Environmental and situational constraints: Frontline staff and travelers may not have time to unlock phones, open apps, or depend on unstable network connections, especially in noisy or high-pressure settings.

These characteristics underscore the need for an offline-capable, real-time, context-aware translation system that fits naturally into live conversations without requiring cumbersome interaction.