Machine translation system development based on human likeness

We present a novel approach for parameter adjustment in empirical machine translation systems. Instead of relying on a single evaluation metric, or in an ad-hoc linear combination of metrics, our method works over metric combinations with maximum descriptive power, aiming to maximise the Human Likeness of the automatic translations. We apply it to the problem of optimising decoding stage parameters of a state- of-the-art Statistical machine translation system. By means of a rigorous manual evaluation, we show how our methodology provides more reliable and robust system configurations than a tuning strategy based on the BLEU metric alone.