Neural Sequence Model Training via $alpha$-divergence Minimization

We propose a new neural sequence model training method in which the objective function is defined by $alpha$-divergence. We demonstrate that the objective function generalizes the maximum-likelihood (ML)-based and reinforcement learning (RL)-based objective functions as special cases (i.e., ML corresponds to $alpha to 0$ and RL to $alpha to1$). We also show that the gradient of the objective function can be considered a mixture of ML- and RL-based objective gradients. The experimental results of a machine translation task show that minimizing the objective function with $alpha > 0$ outperforms $alpha to 0$, which corresponds to ML-based methods.