1. Frustratingly Easy Domain Adaptation
Hal Daume III

2. Introduction
Task: Developing Learning Algorithms that can be easily ported from one domain to another. Example: from newswire to biomedical docs.
particularly interesting in NLP.
Idea: Transforming the domain adaptation learning problem into a standard supervised learning problem to which any standard algorithm may be applied (eg., maxent, SVM)
Transformation is simple – Augment the feature space of both the source and target data and use the result as input to a standard learning algorithm.

3. Problem Formalization
Notation:
X the input space (typically either a real vector or a binary vector) and Y the output space.
Ds to denote the distribution over source examples and Dt to denote the distribution over target examples.
we have access to a samples Ds ∼ Ds of source examples from the source domain, and samples Dt ∼ Dt of target examples from the target domain.
assume that Ds is a collection of N examples and Dt is a collection of M examples (where, typically, N ≫ M).
Goal: to learn a function h : X → Y with low expected loss with respect to the target domain.

4. Adaptation by Feature Augmentation
Take each feature in the original problem and make three versions of it: a general version, a source-specific version and a target-specific version.
Augmented source data = General and source specific
Augmented Target data = General and target specific

5. Results
Tasks (see paper)

6. Experimental Results
See paper