论文标题
神经机器翻译与蒙特卡洛树搜索
Neural Machine Translation with Monte-Carlo Tree Search
论文作者
论文摘要
机器翻译中的最新算法包括一个值网络，以在决定在翻译的每个步骤中输出哪个单词时，可以协助策略网络。价值网络的添加有助于算法在评估指标（例如BLEU得分）上的表现更好。在监督环境中训练政策和价值网络之后，可以通过共同的参与者 - 批判性方法共同改进政策和价值网络。我们项目的主要思想是要利用蒙特卡洛树搜索（MCT）以与Alphazero相似的方式搜索良好的输出单词，并通过合并的策略和价值网络体系结构进行指导。该网络既可以用作本地和全局的外观参考，又是使用搜索结果来改善自身的参考。使用IWLST14德语到英语翻译数据集的实验表明，我们的方法的表现优于最近机器翻译文件中使用的参与者批评方法。
Recent algorithms in machine translation have included a value network to assist the policy network when deciding which word to output at each step of the translation. The addition of a value network helps the algorithm perform better on evaluation metrics like the BLEU score. After training the policy and value networks in a supervised setting, the policy and value networks can be jointly improved through common actor-critic methods. The main idea of our project is to instead leverage Monte-Carlo Tree Search (MCTS) to search for good output words with guidance from a combined policy and value network architecture in a similar fashion as AlphaZero. This network serves both as a local and a global look-ahead reference that uses the result of the search to improve itself. Experiments using the IWLST14 German to English translation dataset show that our method outperforms the actor-critic methods used in recent machine translation papers.
