JACIII Vol.22 No.5 pp. 777-785
doi: 10.20965/jaciii.2018.p0777


Natural Language Generation Using Monte Carlo Tree Search

Kaori Kumagai*1, Ichiro Kobayashi*1, Daichi Mochihashi*2, Hideki Asoh*3, Tomoaki Nakamura*4, and Takayuki Nagai*4

*1Advanced Sciences, Graduate School of Humanities and Sciences, Ochanomizu University
2-1-1 Ohtsuka, Bunkyo-ku, Tokyo 112-8610, Japan

*2The Institute of Statistical Mathematics
10-3 Midori-cho, Tachikawa city, Tokyo 190-0014, Japan

*3National Institute of Advanced Industrial Science and Technology (AIST)
1-1-1 Umezono, Tsukuba, Ibaraki 305-8560, Japan

*4The University of Electro-Communications
1-5-1 Chofugaoka, Chofu, Tokyo 182-8585, Japan

March 17, 2018
July 31, 2018
September 20, 2018
natural language generation, Monte Carlo tree search, syntactic tree, context-free grammar
Natural Language Generation Using Monte Carlo Tree Search

MCTS algorithm for NLG

We propose a method of simulation-based natural language generation that accounts for both building a correct syntactic structure and reflecting the given situational information as input for the generated sentence. We employ the Monte Carlo tree search for this nontrivial search problem in simulation, using context-free grammar rules as search operators. We evaluated numerous generation results from two aspects: the appropriateness of sentence contents for the given input information and the sequence of words in a generated sentence. Furthermore, in order to realize an efficient search in simulation, we introduced procedures to unfold syntactic structures from words strongly related to the given situational information, and increased the probability of selecting those related words. Through a numbers of experiments, we confirmed that our method can effectively generate a sentence with various words and phrasings.

Cite this article as:
Kaori Kumagai, Ichiro Kobayashi, Daichi Mochihashi, Hideki Asoh, Tomoaki Nakamura, and Takayuki Nagai, “Natural Language Generation Using Monte Carlo Tree Search,” J. Adv. Comput. Intell. Intell. Inform., Vol.22, No.5, pp. 777-785, 2018.
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Last updated on Jun. 24, 2022