  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.
