ReVal: A Simple and Effective Machine Translation Evaluation Metric Based on Recurrent Neural Networks

Gupta, R., Orasan, C., & van Genabith, J. (2015). ReVal: A Simple and Effective Machine Translation Evaluation Metric Based on Recurrent Neural Networks. In Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing (pp. 10661072). Lisbon, Portugal.

Abstract

Many state-of-the-art Machine Translation (MT) evaluation metrics are complex, involve extensive external resources (e.g. for paraphrasing) and require tuning to achieve best results. We present a simple alternative approach based on dense vector spaces and recurrent neural networks (RNNs), in particular Long Short Term Memory (LSTM) networks. For WMT-14, our new metric scores best for two out of five language pairs, and overall best and second best on all language pairs, using Spearman and Pearson correlation, respectively. We also show how training data is computed automatically from WMT ranks data

Electronic version

http://aclweb.org/anthology/D15-1124