Softmax Q-Distribution Estimation for Structured Prediction: A Theoretical Interpretation for RAML
Table 8: Corpus-level BLEU score of RAML using importance sampling
['Method', 'Bleu']
The best model (under τ=0.8) is comparable with the one achieved by n-gram replacement. However, n-gram replacement is much simpler to implement, and importance sampling requires extra computation of the proposal distribution and associated importance weights, which would be less computationally efficient. In our experiments, our highly optimized RAML model achieves a training speed of 18,000 words/sec for importance sampling and 21,000 words/sec for n-gram replacement.