Most real world language problems require learning from heterogenous corpora,
raising the problem of learning robust models which generalise well to both
similar (in domain) and dissimilar (out of domain) instances to those seen in
training. This requires learning an underlying task, while not learning
irrelevant signals and biases specific to individual domains. We propose a
novel method to optimise both in- and out-of-domain accuracy based on joint
learning of a structured neural model with domain-specific and domain-general
components, coupled with adversarial training for domain. Evaluating on
multi-domain language identification and multi-domain sentiment analysis, we
show substantial improvements over standard domain adaptation techniques, and
domain-adversarial training.

本文提出了一种基于结构化神经模型与领域特定和领域通用组件的联合学习以及领域对抗训练的方法，以优化域内和域外准确性，本方法在多领域语言识别和情感分析领域中的表现都优于标准领域适应技术和领域对抗培训。