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.

We compare our model with other strong competitive methods on the SQuAD leaderboard and TriviaQA leaderboard.
What other solutions do they compare to?

The attention function BIBREF11 is used to compute the similarity score between passages and questions as: INLINEFORM2
Do they use attention?

In particular, we use Semeval 2014 BIBREF34 Twitter Sentiment Analysis Dataset for the training Sarcasm Datasets Used in the Experiment
This dataset was created by BIBREF8 . The tweets were downloaded from Twitter using #sarcasm as a marker for sarcastic tweets. It is a monolingual English dataset which consists of a balanced distribution of 50,000 sarcastic tweets and 50,000 non-sarcastic tweets.

Since sarcastic tweets are less frequently used BIBREF8 , we also need to investigate the robustness of the selected features and the model trained on these features on an imbalanced dataset. To this end, we used another English dataset from BIBREF8 . It consists of 25,000 sarcastic tweets and 75,000 non-sarcastic tweets.

We have obtained this dataset from The Sarcasm Detector. It contains 120,000 tweets, out of which 20,000 are sarcastic and 100,000 are non-sarcastic. We randomly sampled 10,000 sarcastic and 20,000 non-sarcastic tweets from the dataset. Visualization of both the original and subset data show similar characteristics.
Which benchmark datasets are used?