Pairwise Neural Machine Translation Evaluation
This work addresses the challenge of evaluating machine translation quality for researchers and practitioners, though it appears incremental as it builds on existing neural approaches.
The authors tackled the problem of machine translation evaluation by introducing a neural network framework that selects the better translation from a pair of hypotheses using compact vector representations, achieving correlation with humans that rivals state-of-the-art methods.
We present a novel framework for machine translation evaluation using neural networks in a pairwise setting, where the goal is to select the better translation from a pair of hypotheses, given the reference translation. In this framework, lexical, syntactic and semantic information from the reference and the two hypotheses is compacted into relatively small distributed vector representations, and fed into a multi-layer neural network that models the interaction between each of the hypotheses and the reference, as well as between the two hypotheses. These compact representations are in turn based on word and sentence embeddings, which are learned using neural networks. The framework is flexible, allows for efficient learning and classification, and yields correlation with humans that rivals the state of the art.