We present a 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 embedded into compact distributed vector representations,
and fed into a multi-layer neural network that models nonlinear interactions
between each of the hypotheses and the reference, as well as between the two
hypotheses. We experiment with the benchmark datasets from the WMT Metrics
shared task, on which we obtain the best results published so far, with the
basic network configuration. We also perform a series of experiments to analyze
and understand the contribution of the different components of the network. We
evaluate variants and extensions, including fine-tuning of the semantic
embeddings, and sentence-based representations modeled with convolutional and
recurrent neural networks. In summary, the proposed framework is flexible and
generalizable, allows for efficient learning and scoring, and provides an MT
evaluation metric that correlates with human judgments, and is on par with the
state of the art.

使用神经网络在配对设置下进行机器翻译评估的框架，以从一对假设中选择更好的翻译为目标。该框架将参考和两个假设的词汇、句法和语义信息嵌入到紧凑的分布式向量表示中，并提供了一个与人类判断相关的 MT 评估度量。