TASK DEFINITION: In this task, you will be presented with a context from an academic paper and you have to write an answerable question based on the context. Your questions can be extractive, abstractive, or yes-no questions.
PROBLEM: The neural machine translation was trained using Nematus. For the NMT system as well as for the PreMT system, we used the default configuration.

SOLUTION: Which NMT architecture do they use?

PROBLEM: Second, scrapping Tweets directly from Twitter website. Using the second option, the daily Tweets for stocks of interest from 2015 January to 2017 June were downloaded. For this reason, two companies from the same industry, Tesla and Ford are investigated on how Twitter sentiment could impact the stock price. To translate each tweet into a sentiment score, the Stanford coreNLP software was used.

SOLUTION: Which tweets are used to output the daily sentiment signal?

PROBLEM: To compare our results, we use the provided baseline, which is a non-parameter optimized linear-kernel SVM that uses TF-IDF bag-of-word vectors as inputs. 

SOLUTION:
What was the baseline model?