In this task, you will be presented with a context from an academic paper and a question separated with a 
. You have to answer the question based on the context.

Example Input: 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?
Example Output: Accuracy and F1 score for supervised tasks, Pearson's and Spearman's correlation for unsupervised tasks

Example Input: The UNGA speeches dataset, compiled by Baturo et al. UNGAspeeches, contains the text from 7,507 speeches given between 1970-2015 inclusive. 
 Question: how many speeches are in the dataset?
Example Output: 7,507

Example Input: To address our next question, we assessed our pipeline when learning relations independently (i.e., individually) versus learning relations jointly within the RDN, displayed in Table TABREF22 . 
 Question: What do they learn jointly?
Example Output:
relations

