Paper: Structural and Topical Dimensions in Multi-Task Patent Translation

ACL ID E12-1083
Title Structural and Topical Dimensions in Multi-Task Patent Translation
Venue Annual Meeting of The European Chapter of The Association of Computational Linguistics
Session Main Conference
Year 2012
Authors

Patent translation is a complex problem due to the highly specialized technical vocab- ulary and the peculiar textual structure of patent documents. In this paper we analyze patents along the orthogonal dimensions of topic and textual structure. We view differ- ent patent classes and different patent text sections such as title, abstract, and claims, as separate translation tasks, and investi- gate the influence of such tasks on machine translation performance. We study multi- task learning techniques that exploit com- monalities between tasks by mixtures of translation models or by multi-task meta- parameter tuning. We find small but sig- nificant gains over task-specific training by techniques that model commonalities through shared parameters. A by-product of our work is a parallel pate...