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.
For example, REF use Tree Long Short Term Memory (Tree-LSTM) based networks for reference-based MT evaluation.
ReVal: A Simple and Effective Machine Translation Evaluation Metric Based on Recurrent Neural Networks
EMNLP
Computer Science