Neural Machine Translation with Monte-Carlo Tree Search
This is an incremental improvement for machine translation researchers, offering a specific algorithmic enhancement.
The paper tackles neural machine translation by replacing actor-critic methods with Monte-Carlo Tree Search (MCTS) guided by a combined policy-value network, achieving better performance on the IWLST14 German-English dataset compared to recent methods.
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.