HIP Domain Translate: A Linguistically-Grounded HCI Approach to Domain Adaptation
Rebecca Li Hao · Digital Access to Scholarship at Harvard (DASH) (Harvard University) · 2020
Machine translation (MT) has enabled immense communication and shared knowledge throughout the world, yet it still falls short. Specifically, when these systems lack data in a particular domain, their accuracy plummets. Considerable work in data-based and model-based approaches have improved the accuracy of domain-specific MT, but these do not directly address ambiguities and decisions of style that are context-dependent. In this thesis, I present Human-Intelligence Powered (HIP) Domain Translate, a system that leverages post-edits made by users to machine translated texts by generating "fixes" within a domain for users to apply. Though interviews, the collection and analysis of post-edits, and a preliminary six-person user study on the prototype, I demonstrate that this system shows promise as a method to quickly and effectively capture contextual information within a domain that is missing in machine translation outputs.