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: Using our methodology, we tagged 10,000 Arabic tweet dataset for offensiveness, where offensive tweets account for roughly 19% of the tweets. Further, we labeled tweets as vulgar or hate speech.  
 Question: How many tweets are in the dataset?
Example Output: 10,000 Arabic tweet dataset 

Example Input: EmotionLines BIBREF6 is a dialogue dataset composed of two subsets, Friends and EmotionPush, according to the source of the dialogues. 
 Question: what datasets were used?
Example Output: Friends EmotionPush

Example Input: KGR10, also known as plWordNet Corpus 10.0 (PLWNC 10.0), is the result of the work on the toolchain to automatic acquisition and extraction of the website content, called CorpoGrabber BIBREF19 . It is a pipeline of tools to get the most relevant content of the website, including all subsites (up to the user-defined depth). The proposed toolchain can be used to build a big Web corpus of text documents. It requires the list of the root websites as the input. Tools composing CorpoGrabber are adapted to Polish, but most subtasks are language independent. The whole process can be run in parallel on a single machine and includes the following tasks: download of the HTML subpages of each input page URL with HTTrack, extraction of plain text from each subpage by removing boilerplate content (such as navigation links, headers, footers, advertisements from HTML pages) BIBREF20 , deduplication of plain text BIBREF20 , bad quality documents removal utilising Morphological Analysis Converter and Aggregator (MACA) BIBREF21 , documents tagging using Wrocław CRF Tagger (WCRFT) BIBREF22 . Last two steps are available only for Polish. 
 Question: How was the KGR10 corpus created?
Example Output:
most relevant content of the website, including all subsites