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


question:
In the sentiment analysis experiments, we found that a tweet may contain multiple sentiments. The groundtruth labels contain 210 positive sentiments, 521 neutral sentiments, and 305 negative sentiments to the candidates.
answer:
Is datasets for sentiment analysis balanced?


question:
Thus we consider the task of answer generation for TweetQA and we use several standard metrics for natural language generation to evaluate QA systems on our dataset, namely we consider BLEU-1 BIBREF16 , Meteor BIBREF17 and Rouge-L BIBREF18 in this paper.
answer:

What evaluation metrics do they use?