As described in the question-answer writing process, the answers in our dataset are different from those in some existing extractive datasets.
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 5 (Papineni et al., 2002), Meteor (Denkowski and Lavie, 2011) and Rouge-L (Lin, 2004) in this paper.