BattRAE: Bidimensional Attention-Based Recursive Autoencoders for Learning Bilingual Phrase Embeddings
Table 2: Experiment results on the MT 06/08 test sets. AVG = average BLEU scores for test sets. We highlight the best result in bold. “⇑”: significantly better than Baseline (p < 0.01); “∗∗”: significantly better than BRAE (p < 0.01); “↑”: significantly better than BCorrRAE (p < 0.05).
['[BOLD] Method', '[BOLD] MT06', '[BOLD] MT08', '[BOLD] AVG']
BattRAE significantly improves translation quality on all test sets in terms of BLEU. Especially, it achieves an improvement of up to 1.63 BLEU points on average over the Baseline. Comparing with the two neural baselines, our BattRAE model obtains consistent improvements on all test sets. It significantly outperforms BCorrRAE by 0.48 BLEU points, and BRAE by almost 1 BLEU point on average. We contribute these improvements to the incorporation of clues and interactions at different levels of granularity since neither BCorrRAE nor BRAE explore them.