Response-based learning for patent translation
Stefan Riezler · 2015
In response-based structured prediction, instead of a gold-standard structure, the learner is given a response to a predicted structure from which a supervision signal for structured learning is extracted. Applied to statistical machine translation (SMT), different types of environments such as a downstream application, a professional translator, or an SMT user, may respond to predicted translations with a ranking, a correction, or an acceptance/rejection decision, respec-tively. We present algorithms and experiments that show that learning from responses alleviates the supervision problem and allows a direct optimization of SMT for tasks such as cross-lingual patent prior art retrieval, or translation of technical patent documents. 1