论文标题
通过协调域编码和配对分类器的多源域适应
Multiple-Source Domain Adaptation via Coordinated Domain Encoders and Paired Classifiers
论文作者
论文摘要
我们提出了一个新颖的多源无监督模型，用于在域移动下进行文本分类。我们的模型利用文档表示中的更新速率来动态整合域编码器。它还采用概率启发式方法来推断目标域中的错误率以配对源分类器。我们的启发式利用数据转换成本和目标特征空间中的分类器精度。我们已经使用了现实世界的适应性场景来评估我们的算法的功效。我们还使用验证的多层变压器作为实验中的文档编码器，以证明是否可以通过开箱即用的语言模型预处理进行域名适应模型所实现的改进。实验证明我们的模型是这种情况下的最佳性能方法。
We present a novel multiple-source unsupervised model for text classification under domain shift. Our model exploits the update rates in document representations to dynamically integrate domain encoders. It also employs a probabilistic heuristic to infer the error rate in the target domain in order to pair source classifiers. Our heuristic exploits data transformation cost and the classifier accuracy in the target feature space. We have used real world scenarios of Domain Adaptation to evaluate the efficacy of our algorithm. We also used pretrained multi-layer transformers as the document encoder in the experiments to demonstrate whether the improvement achieved by domain adaptation models can be delivered by out-of-the-box language model pretraining. The experiments testify that our model is the top performing approach in this setting.
