  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.
