In this paper, we propose conditional adversarial networks (CANs), a
framework that explores the relationship between the shared features and the
label predictions to impose more discriminability to the shared features, for
multi-domain text classification (MDTC). The proposed CAN introduces a
conditional domain discriminator to model the domain variance in both shared
feature representations and class-aware information simultaneously and adopts
entropy conditioning to guarantee the transferability of the shared features.
We provide theoretical analysis for the CAN framework, showing that CAN's
objective is equivalent to minimizing the total divergence among multiple joint
distributions of shared features and label predictions. Therefore, CAN is a
theoretically sound adversarial network that discriminates over multiple
distributions. Evaluation results on two MDTC benchmarks show that CAN
outperforms prior methods. Further experiments demonstrate that CAN has a good
ability to generalize learned knowledge to unseen domains.

本文提出了条件对抗网络 (CANs) 框架，旨在探索共享特征和标签预测之间的关系，以对共享特征施加更多的可辨识性，用于多领域文本分类 (MDTC)。CAN 引入了一个条件域鉴别器，同时对共享特征表示和类别感知信息建模，采用熵条件保证共享特征的可转移性。我们提供了 CAN 框架的理论分析，证明 CAN 的目标等价于最小化共享特征和标签预测的多个联合分布之间的总发散。因此，CAN 是一个理论上良好的对抗网络，其可以判别多个分布。在两个 MDTC 基准测试上的评估结果表明，CAN 优于之前的方法。进一步的实验表明，CAN 具有将学习到的知识推广到未见领域的良好能力。