Incorporating Structural Alignment Biases into an Attentional Neural Translation Model
Table 2: Perplexity results for attentional model variants evaluated on BTEC zh→en, and number of model parameters (in millions).
['[BOLD] configuration', '[BOLD] test', '[BOLD] #param (M)']
All other results are for the attentional model with a single-layer LSTM as encoder and two-layer LSTM as decoder, using 512 embedding, 512 hidden, and 256 alignment dimensions. For each model, we also report the number of its parameters. Models are trained using stochastic gradient, allowing up to 20 epochs. For each model the best perplexity on the held-out development set is reported, which was achieved in 5-10 epochs for most cases.