Label Embedding Network: Learning Label Representation for Soft Training of Deep Networks
Table 4: Results of Label Embedding for the IWSLT2015 machine translation task. The evaluation metric is BLEU score (higher is better).
['[BOLD] IWSLT2015', 'BLEU']
Then, we show experimental results on the IWSLT2015 machine translation task. We measure the quality of the translation by BLEU, following common practice. The proposed method achieves better BLEU score than the baseline, with an improvement of 1.1 points. To our knowledge, 26.8 is the highest BLEU achieved on the task, surpassing the previous best result 26.1 From the experimental results, it is clear that the compressed label embedding can improve the results of the Seq-to-Seq model as well, and works for the tasks, where there is a massive number of labels.