Pairwise Neural Machine Translation Evaluation
Abstract
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.
Cite
@article{arxiv.1912.03135,
title = {Pairwise Neural Machine Translation Evaluation},
author = {Francisco Guzman and Shafiq Joty and Lluis Marquez and Preslav Nakov},
journal= {arXiv preprint arXiv:1912.03135},
year = {2019}
}
Comments
machine translation evaluation, machine translation, pairwise ranking, learning to rank. arXiv admin note: substantial text overlap with arXiv:1710.02095