Task: Question Answering
Input (English): We use a standard set of supervised as well as unsupervised benchmark tasks from the literature to evaluate our trained models, following BIBREF16 Sentence embeddings are evaluated for various supervised classification tasks as follows. The predefined training split is used to tune the L2 penalty parameter using cross-validation and the accuracy and F1 scores are computed on the test set.  We perform unsupervised evaluation of the learnt sentence embeddings using the sentence cosine similarity, on the STS 2014 BIBREF31 and SICK 2014 BIBREF32 datasets. These similarity scores are compared to the gold-standard human judgements using Pearson's INLINEFORM0 BIBREF33 and Spearman's INLINEFORM1 BIBREF34 correlation scores.  
 Question: What metric is used to measure performance?
Output (English): Accuracy and F1 score for supervised tasks, Pearson's and Spearman's correlation for unsupervised tasks