Optimized MT Online Learning in Computer Assisted Translation
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Abstract
In this paper we propose a cascading framework for optimizing online learning in
machine translation for computer assisted translation scenario.
With the use of online learning, one introduces several hyper parameters
associated with the learning algorithm.
Number of iterations of online learning can affect the quality of translation as
well.
We discuss these issues and propose a few approaches that can be used to
optimize the hyper parameters and also to find the number of iterations required
for online learning.
We experimentally show that using optimal number of iterations in online
learning proves to be useful and we get consistent improvement against baseline
results