Publication
A Feedback-enabled Machine Learning Approach for Multi-Engine Machine Translation
Christian Federmann
In: Proceedings of the AAAI 2013 Spring Symposium on Lifelong Machine Learning. AAAI Spring Symposium (AAAI SSS-13), Stanford, CA, USA, AAAI Press, 3/2013.
Abstract
We describe an approach for multi-engine machine
translation that uses machine learning methods to train
one or several classifiers for a given set of candidate
translations. Contrary to existing approaches in quality
estimation which only consider a single translation at
a time, we explicitly model pairwise comparison with
our feature vectors. We discuss several challenges our
method is facing and discuss how lifelong machine
learning could be applied to resolve these.We also show
how the proposed architecture can be extended to allow
human feedback to be included into the training process,
improving the system’s selection process over time.