Machine Translation Evaluation with Neural Networks
Table 7: Kendall’s τ on WMT12 for different variants of LSTM-RNNs.
['[BOLD] Details', '[BOLD] Kendall’s  [ITALIC] τ  [BOLD] cz', '[BOLD] Kendall’s  [ITALIC] τ  [BOLD] de', '[BOLD] Kendall’s  [ITALIC] τ  [BOLD] es', '[BOLD] Kendall’s  [ITALIC] τ  [BOLD] fr', '[BOLD] Kendall’s  [ITALIC] τ  [BOLD] AVG']
The first row shows the results for the averaging baseline (no semantic composition using CNN or LSTM). The second and the third rows show the results for our CNN model: when it is initialized with pretrained Wiki-GW50 vectors, and when it is randomly initialized, respectively. We can see that CNN with pre-trained vectors is slightly better than our averaging BOW baseline.