Fig. 2 28th Ryuou Tournament, Match 5, 59th move. The last move is Black s Rx5f. 1 Tic-Tac-Toe Fig. 1 AsearchtreeofTic-Tac-Toe. [2] [3], [4]
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1 1,a) , Predicting Moves in Comments for Shogi Commentary Generation Hirotaka Kameko 1,a) Shinsuke Mori 2 Yoshimasa Tsuruoka 3 Received: April 6, 2017, Accepted: September 5, 2017 Abstract: In this paper, we propose a method for predicting the moves that should be mentioned in Shogi commentaries. We train the prediction model using commented trees, which show the moves in comments by human experts. Then we predict the commented trees for the states of Shogi using the prediction model and the result of searching. Our proposed method outperforms the baseline and our method may capture some properties of the moves mentioned in commentaries. In addition, the results show that our method can generate some commented trees. Keywords: Shogi, commentary, natural language generation 1. 1 Graduate School of Engineering, The University of Tokyo, Bunkyo, Tokyo , Japan 2 Academic Center for Computing and Media Studies, Kyoto University, Kyoto , Japan 3 Graduate School of Information Science and Technology, The University of Tokyo, Bunkyo, Tokyo , Japan a) kameko@logos.t.u-tokyo.ac.jp [1] c 2017 Information Processing Society of Japan 2070
2 Fig. 2 28th Ryuou Tournament, Match 5, 59th move. The last move is Black s Rx5f. 1 Tic-Tac-Toe Fig. 1 AsearchtreeofTic-Tac-Toe. [2] [3], [4] [5], [6] [7] 1 Tic-Tac-Toe 1 1b 3c 3c 1a 2c 2c 1a 80 [8] 2 2 [9] Sadikov [10] 2 c 2017 Information Processing Society of Japan 2071
3 Black has an active bishop pair. N [11] [12] Sadikov [1], [13] [3] 2.2 [2] [14] [15] [16] 2 s m P (m s) = 1 1+exp( w T φ(m, s)) (1) w φ(m, s) s m 3. [13] 3 c 2017 Information Processing Society of Japan 2072
4 4. 3 Fig. 3 Examples of commented trees (red box) and candidate trees. 3 3 [13] (1) [13] [2] w/passmove w/opassmove 3 S A S B S A w/passmove w/passmove S B w/opassmove c 2017 Information Processing Society of Japan 2073
5 4 Fig. 4 The procedure of generating a commented tree. Green edges are the moves which are obtained by searching and orange edges are the moves which are obtained by the prediction model. w/opassmove w/opassmove w/passmove S A w/passmove S B w/opassmove w/opassmove Algorithm 1 Pseudo code of generating commented trees. Initialize Tree with the current state 1) function ExpandNode(Tree, State, Depth) BestMoveSequence Search(State) if Depth =0then Expand all moves in BestMoveSequence else // Expand the best move by searching 2) BestMove BestMoveSequence S best State,BestMove Expand Tree with BestMove ExpandNode(Tree, S best, Depth 1) // Expand moves with Pass by prediction 3) P assmovelist Prediction pass (State) Sort P assmovelist by probability for P assmove P assmovelist do if P (P assmove State) < threshold pass then break end if S passmove State, P assmove Expand T ree with P assmove 4) ExpandNode(Tree, S passmove, Depth 1) end for // Expand moves without Pass by prediction 3) MoveList Prediction wopass (State) Sort MoveList in the probability for Move MoveList do if Move = BestMove then continue end if if P (Move State) < threshold wopass then break end if S move State,Move Expand Tree with Move 4) ExpandNode(Tree, S move, Depth 1) end for end if end function Algorithm 1 4 1) 2) c 2017 Information Processing Society of Japan 2074
6 3) w/passmove w/opassmove 4) 5) 2) 4) [13] [16] A B 2 *1 3,664 55, ,842 5,000 6,000 w/opassmove 96,908 w/passmove 18,706 w/opassmove 16,589 w/passmove 4,472 w/opassmove *1 5 Precision-Recall w/opassmove w/passmove Fig. 5 Precision-Recall curves of the move prediction model in commented trees. Upper: w/opassmove Lower: w/ PassMove. wopass w/passmove wpass Together Baseline 4 40,000 5 PrecisionRecall w/opassmove w/passmove 500 c 2017 Information Processing Society of Japan 2075
7 1 w/opassmovew/ PassMove Table 1 Relationship between the battle phase and move prediction. Upper: w/opassmove Lower: w/passmove. Precision Recall F-Score Precision Recall F-Score wopasswpass Fig. 6 Contribution of the additional features. Upper: wopass Lower: wpass. w/passmove w/opassmove w/passmove w/opassmove F1 wopass Precision = Recall = p =0.562 wpass Precision = Recall = p = wopass wpass wpass F 1 31 w/passmove w/opassmove w/opassmove p =0.65 w/passmove p = w/opassmove p =0.5 w/passmove p =0.3 2) 4) 3 8 w/opassmove c 2017 Information Processing Society of Japan 2076
8 Fig. 7 57th Oui Tournament, Match 6, 6th move. The last move is White s P-9d * * Fig. 8 28th Ryuou Tournament, Match 5, 59th move. The last move is Black s Bx5f. w/passmove * *3 html c 2017 Information Processing Society of Japan 2077
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10 ACL AI c 2017 Information Processing Society of Japan 2079
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The copyright of this material is retained by the Information Processing Society of Japan (IPSJ). The material has been made available on the website
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