主催: The Japanese Society for Artificial Intelligence
会議名: 2017年度人工知能学会全国大会(第31回)
回次: 31
開催地: 愛知県名古屋市 ウインクあいち
開催日: 2017/05/23 - 2017/05/26
We characterize a tree mapping search space in terms of the tree fragment depth and number of variables, which are parameters of the resulting tree transducer grammar. We show how such characterization explains the trade-off between computational complexity and tree transducer expressivity. We evaluate our induced tree transducers on a Question-Answering task, quantifying accuracy and average tree mapping time as a function of our parameterization.