Auto-Adaptive Learning from Weak Feedback for Interactive Lecture Translation

• Applicant: Professor Dr. Stefan Riezler
• Subject Area: Image and Language Processing, Computer Graphics and Visualisation, Human Computer Interaction, Ubiquitous and Wearable Computing ; General and Comparative Linguistics, Experimental Linguistics, Typology, Non-European Languages
• Term: from 2017 to 2022
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 326904228

The goal of the proposed project is to enable the use statistical machine translation (SMT) for the difficult task of translation of university lectures. This is done by enhancing SMT by a mutually beneficial learning cycle, called auto-adaptive SMT, that incorporates the human for post-editing SMT output from which the system can learn immediately. In the ``traditional'' setup, post-editors are instructed to produce a perfect translation, which can be very resource-intensive, not only in terms of editing time but also in terms of a user's required language proficiency. In this project, it is our main goal to explore ways of learning from weaker feedback than a full post-edit. This feedback could consist of partial corrections or merely judgments on the quality of the SMT output. A central point is that in order to guarantee machine learnability, human feedback needs to contain a signal strong enough for statistical learning. This means that we are faced with a trade-off between machine learnability and elicitability of feedback from human users, which we will attempt to solve. Our research will focus on the design of efficient algorithms that perform learning from weak feedback, and on frontend/backend interfaces that support a practical use of the algorithms in field tests of interactive translation of university lectures.

• DFG Programme: Research Grants