Depth-Gated LSTM
Table 2: BLEU scores by reranking on BTEC Chinese to English machine translation task
['[BOLD] Dataset', '[BOLD] Baseline', '[BOLD] DGLSTM']
In another experiment for the machine translation experiment, we used attention model with DGLSTM to rescore test set k-best lists. We first trained two attention models, one was with 3 layers of DGLSTMs and the other was with 5 layers of DGLSTMs, on training set. Both used 50-dimension hidden layers. We then trained a reranker model using the development data with 100-best lists for each translation pair. The top 100 best lists were generated from the baseline. The features for the reranker models are the scores from the attention model. The 100-best lists on the test set were reranked using the trained reranker model. We ran the above described reranking processes 10 times to get an averaged BLEU scores, which was obtained using one reference. Compared to the baseline, DGLSTM improved BLEU scores by 3 points on the Test set.