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arXiv · 1706.10031

Neural Sequence Model Training via $α$-divergence Minimization

Sotetsu Koyamada·Yuta Kikuchi·Atsunori Kanemura·Shin-ichi Maeda·Shin Ishii
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Abstract

We propose a new neural sequence model training method in which the objective function is defined by $α$-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 $α\to 0$ and RL to $α\to1$). 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 $α> 0$ outperforms $α\to 0$, which corresponds to ML-based methods.

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Sotetsu Koyamada, Yuta Kikuchi, Atsunori Kanemura, Shin-ichi Maeda, Shin Ishii. 2017-06-30. Neural Sequence Model Training via $α$-divergence Minimization. https://arxiv.org/abs/1706.10031

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