Beam search is the go-to method for decoding auto-regressive machine
translation models. While it yields consistent improvements in terms of BLEU,
it is only concerned with finding outputs with high model likelihood, and is
thus agnostic to whatever end metric or score practitioners care about. Our aim
is to establish whether beam search can be replaced by a more powerful
metric-driven search technique. To this end, we explore numerous decoding
algorithms, including some which rely on a value function parameterised by a
neural network, and report results on a variety of metrics. Notably, we
introduce a Monte-Carlo Tree Search (MCTS) based method and showcase its
competitiveness. We provide a blueprint for how to use MCTS fruitfully in
language applications, which opens promising future directions. We find that
which algorithm is best heavily depends on the characteristics of the goal
metric; we believe that our extensive experiments and analysis will inform
further research in this area.

本文探讨是否可以将 Beam Search 替换为更强大的以度量为驱动的搜索技术。通过对多个解码算法的探索和分析，发现最佳算法取决于目标度量的特性；作者提出的基于 Monte-Carlo Tree Search (MCTS) 的搜索方法在语言应用中具有很高的应用价值，为今后的研究方向提供了新视角。