Statistical Machine Translation Features with Multitask Tensor Networks
Table 3: Experimental results to investigate the effects of the new features, DTN and MTL. The top part shows the BOLT results, while the bottom part shows the NIST results. The best results for each conditions and each language-pair are in bold. The baselines are in italics. .
['Feature set', 'AR-EN NN', 'AR-EN Tensor', 'AR-EN MTL', 'ZH-EN NN', 'ZH-EN Tensor', 'ZH-EN MTL']
For MTL, we use L=3,t=2 since it gives the best intrinsic score. Employing tensors instead of regular neural networks gives a significant and consistent positive impact for all models and language pairs. For the system with the baseline features, we use the tensor architecture for both the joint model and the lexical translation model of Devlin et al. resulting in an improvement of around 0.7 BLEU points, and showing the wide applicability of the tensor architecture. On top of this improved baseline, we also observe an improvement of the same scale for other models (column “Tensor”), except for HypEn features in AR-EN experiment. Moving to MTL experiments, we see improvements, especially from SrcEn features. MTL gives around 0.5 BLEU point improvement for AR-EN and around 0.4 BLEU point for ZH-EN. When we employ both HypEn and SrcEn together, MTL gives around 0.4 BLEU point in AR-EN and 0.2 BLEU point in ZH-EN.