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.

Most lexical resources for sentiment analysis are in English. To still be able to benefit from these sources, the lexicons in the AffectiveTweets package were translated to Spanish, using the machine translation platform Apertium BIBREF5  
 Question: How was the training data translated?
using the machine translation platform Apertium 

Wikilinks can be seen as a large-scale, naturally-occurring, crowd-sourced dataset where thousands of human annotators provide ground truths for mentions of interest. This means that the dataset contains various kinds of noise, especially due to incoherent contexts. We prepare our dataset from the local-context version of Wikilinks, and resolve ground-truth links using a Wikipedia dump from April 2016. We use the page and redirect tables for resolution, and keep the database pageid column as a unique identifier for Wikipedia entities. We discard mentions where the ground-truth could not be resolved (only 3% of mentions). 
 Question: How was a quality control performed so that the text is noisy but the annotations are accurate?
The authors believe that the Wikilinks corpus  contains ground truth annotations while being noisy. They discard mentions that cannot have ground-truth verified by comparison with Wikipedia.

BIBREF14 introduce EMPATHETICDIALOGUES dataset, a novel dataset containing 25k conversations include emotional contexts information to facilitate training and evaluating the textual conversational system. Then, work from BIBREF2 produce a dataset containing 1.5 million Twitter conversation, gathered by using Twitter API from customer care account of 62 brands across several industries. This dataset was used to build tone-aware customer care chatbot. Finally, BIBREF29 tried to enhance SEMAINE corpus BIBREF30 by using crowdsourcing scenario to obtain a human judgement for deciding which response that elicits positive emotion.  
 Question: What are the currently available datasets for EAC?

EMPATHETICDIALOGUES dataset a dataset containing 1.5 million Twitter conversation, gathered by using Twitter API from customer care account of 62 brands across several industries SEMAINE corpus BIBREF30