Extending Anchored Learning to Machine Translation Evaluation

We compare two methods, Anchored Learning, and a simple new method (Hyperplane Distance), for finding Hard To Learn examples in Machine Learning tasks that use SVMs. These Hard To Learn examples can be corpus errors, or examples which are difficult to predict with the given set of features. The Anchored Learning method is extended to deal with various kernels and SV regression. A number of experiments are conducted on toy data sets and standard ML data sets to characterize their performance. Finally, we present an in-depth application to the task of Machine Translation Evaluation to improve our prior work in this area.