Statistical Machine Translation Features with Multitask Tensor Networks
Table 4: Experimental results for the NIST condition. Mixed-case scores are also reported. Baselines are in italics.
['[EMPTY]', 'Base.', 'Feat', 'Tensor', 'MTL']
In particular, we investigate the impact of deploying our new features (column “Feat”) and demonstrate the effects of the tensor architecture (column “Tensor”) and multitask learning (column “MTL”). As shown the results are inline with the BOLT condition where we observe additive improvements from adding our new features, applying tensor network and multitask learning. On Arabic-English, we see a gain of 2.7 BLEU point and on Chinese-English, we see a 1.9 BLEU point gain. We also report the mixed-cased BLEU scores for comparison with previous best published results, i.e. \newcitedevlin-EtAl:2014: P14-1 report 52.8 BLEU for Arabic-English and 34.7 BLEU for Chinese-English. Thus, our results are around 1.3-1.4 BLEU point better. Note that they use additional rescoring features but we do not.