4.1 Dialect ID Binary Classification

We run the sentence through the Dialect ID binary classifier and we use the predicted class label (DA or MSA) as a feature in our system.

It improves over our best baseline single MT system by 1.3% BLEU and over the Dialect ID Binary Classification system selection baseline by 0.8% BLEU.

Next, we train a binary classification model, 6, using those feature vectors.

Any binary classification algorithm can be used to learn 6.

Finally, we classify h using the trained binary classifier 6.

Linear predictors are then learnt to predict the occurrence of those pivots, and SVD is used to construct a lower dimensional representation in which a binary classifier is trained.

The created thesaurus is used to expand feature vectors during train and test stages in a binary classifier .

We report both OVR performance and the performance of three One-versus-One binary classifiers , trained to distinguish between each pair of classes.

We also observe that each of the three One-versas-One binary classifications performs significantly better than chance, suggesting that Employee, Customer, and Tarker are in fact three different classes.

For simplicity, we focus on truthful (Cas-tomer) versus deceptive (Turker) binary classification rather than a multi-class classification.

For each domain in YAGO, we have a binary classification task: whether the instance has the relation corresponding to the domain.

No-transfer classifier (NT) We only use the few labeled instances of the target relation type together with the negative relation instances to train a binary classifier .

Alternate no-transfer classifier (NT-U) We use the union of the k source-domain labeled data sets Dss and the small set of target-domain labeled data D; to train a binary classifier .