Paper: A Multi-Domain Translation Model Framework for Statistical Machine Translation

ACL ID P13-1082
Title A Multi-Domain Translation Model Framework for Statistical Machine Translation
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 2013
Authors

While domain adaptation techniques for SMT have proven to be effective at im- proving translation quality, their practical- ity for a multi-domain environment is of- ten limited because of the computational and human costs of developing and main- taining multiple systems adapted to differ- ent domains. We present an architecture that delays the computation of translation model features until decoding, allowing for the application of mixture-modeling techniques at decoding time. We also de- scribe a method for unsupervised adapta- tion with development and test data from multiple domains. Experimental results on two language pairs demonstrate the effec- tiveness of both our translation model ar- chitecture and automatic clustering, with gains of up to 1 BLEU over unadapted sys- tems and sin...