Neural Machine Translation with Monte-Carlo Tree Search

• URL: http://arxiv.org/abs/2004.12527v1
• Date: Mon, 27 Apr 2020 01:03:26 GMT
• Title: Neural Machine Translation with Monte-Carlo Tree Search
• Abstract summary: 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 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. Experiments using the IWLST14 German to English translation dataset show that our method outperforms the actor-critic methods used in recent machine translation papers.
• Score: 3.8073142980733
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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