Taxonomy is formulated as directed acyclic concepts graphs or trees that support many downstream tasks. Many new coming concepts need to be added to an existing taxonomy. The traditional taxonomy expansion task aims only at finding the best position for new coming concepts in the existing taxonomy. However, they have two drawbacks when being applied to the real-scenarios. The previous methods suffer from low-efficiency since they waste much time when most of the new coming concepts are indeed noisy concepts. They also suffer from low-effectiveness since they collect training samples only from the existing taxonomy, which limits the ability of the model to mine more hypernym-hyponym relationships among real concepts. This paper proposes a pluggable framework called Generative Adversarial Network for Taxonomy Entering Evaluation (GANTEE) to alleviate these drawbacks. A generative adversarial network is designed in this framework by discriminative models to alleviate the first drawback and the generative model to alleviate the second drawback. Two discriminators are used in GANTEE to provide long-term and short-term rewards, respectively. Moreover, to further improve the efficiency, pre-trained language models are used to retrieve the representation of the concepts quickly. The experiments on three real-world large-scale datasets with two different languages show that GANTEE improves the performance of the existing taxonomy expansion methods in both effectiveness and efficiency.

翻译：摘要：分类体系被形式化为支持众多下游任务的有向无环概念图或概念树。许多新兴概念需要被添加至现有分类体系中。传统分类体系扩展任务仅旨在为新兴概念在现有分类体系中寻找最佳位置。然而，当应用于真实场景时，这些方法存在两个缺陷：先前方法由于将大部分时间浪费在（处理）事实上属于噪声概念的新兴概念上，导致效率低下；同时由于仅从现有分类体系中收集训练样本，限制了模型挖掘真实概念间上下位关系的能力，导致有效性不足。本文提出了一种名为"面向分类体系准入评估的生成对抗网络"（GANTEE）的可插拔框架以缓解上述缺陷。该框架通过设计生成对抗网络，利用判别模型缓解第一个缺陷，利用生成模型缓解第二个缺陷。GANTEE采用两个判别器分别提供长期奖励与短期奖励。此外，为进一步提升效率，本文采用预训练语言模型快速检索概念表征。在三个涵盖两种不同语言的大规模真实数据集上的实验表明，GANTEE在有效性与效率两方面均提升了现有分类体系扩展方法的性能。