Aspect-augmented Adversarial Networks for Domain Adaptation

Understand

We introduce a neural method for transfer learning between two (source and target) classification tasks or aspects over the same domain.

• Rather than training on target labels, we use a few keywords pertaining to source and target aspects indicating sentence relevance instead of document class labels.
• Documents are encoded by learning to embed and softly select relevant sentences in an aspect-dependent manner.
• A shared classifier is trained on the source encoded documents and labels, and applied to target encoded documents.