We present a novel end-to-end reinforcement learning approach to automatic
taxonomy induction from a set of terms. While prior methods treat the problem
as a two-phase task (i.e., detecting hypernymy pairs followed by organizing
these pairs into a tree-structured hierarchy), we argue that such two-phase
methods may suffer from error propagation, and cannot effectively optimize
metrics that capture the holistic structure of a taxonomy. In our approach, the
representations of term pairs are learned using multiple sources of information
and used to determine \textit{which} term to select and \textit{where} to place
it on the taxonomy via a policy network. All components are trained in an
end-to-end manner with cumulative rewards, measured by a holistic tree metric
over the training taxonomies. Experiments on two public datasets of different
domains show that our approach outperforms prior state-of-the-art taxonomy
induction methods up to 19.6\% on ancestor F1.

本文提出一种新颖的强化学习方法，旨在从一组术语中自动归纳出分类法，该方法采用多种信息源学习术语对的表示，并使用策略网络确定要选择哪个术语以及将其放置在分类法中的何处，在训练分类法时通过整体树度量进行累积奖励，实验证明，该方法在两个不同领域的两个公共数据集上优于先前的最先进的分类法归纳方法高达 19.6％的祖先 F1。