Machine translation benefits from two types of decoding techniques: consensus decoding over multiple hypotheses under a single model and system combination over hypotheses from di...
John DeNero, Shankar Kumar, Ciprian Chelba, Franz ...
This paper presents collaborative decoding (co-decoding), a new method to improve machine translation accuracy by leveraging translation consensus between multiple machine transla...
Mu Li, Nan Duan, Dongdong Zhang, Chi-Ho Li, Ming Z...
Given a number of machine translations of a source segment, the goal of system combination is to produce a new translation that has better quality than all of them. This paper des...
Statistical machine translation is often faced with the problem of combining training data from many diverse sources into a single translation model which then has to translate se...
Majid Razmara, George Foster, Baskaran Sankaran, A...
In this paper, we propose a novel framework to enrich Translation Memory (TM) systems with Statistical Machine Translation (SMT) outputs using ranking. In order to offer the human...