Open world classification is a task in natural language processing with key
practical relevance and impact. Since the open or {\em unknown} category data
only manifests in the inference phase, finding a model with a suitable decision
boundary accommodating for the identification of known classes and
discrimination of the open category is challenging. The performance of existing
models is limited by the lack of effective open category data during the
training stage or the lack of a good mechanism to learn appropriate decision
boundaries. We propose an approach based on \underline{a}daptive
\underline{n}egative \underline{s}amples (ANS) designed to generate effective
synthetic open category samples in the training stage and without requiring any
prior knowledge or external datasets. Empirically, we find a significant
advantage in using auxiliary one-versus-rest binary classifiers, which
effectively utilize the generated negative samples and avoid the complex
threshold-seeking stage in previous works. Extensive experiments on three
benchmark datasets show that ANS achieves significant improvements over
state-of-the-art methods.

提出基于自适应负样本 (ANS) 的方法，设计可在训练阶段生成有效的合成开放类别样本，并在不需要任何先前知识或外部数据集的情况下使用。采用辅助的一对其余二元分类器，通过利用生成的负样本避免了复杂的阈值寻找阶段。在三个基准数据集上进行的大量实验表明，ANS 比现有方法取得了显著的改进。