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:
Finally, the decoder task, which predicts the target sequence probability at time INLINEFORM3 based on previous output and context information INLINEFORM4 , can be formulated as: DISPLAYFORM0
answer:
How does their decoder generate text?


question:
We use two popular knowledge bases: WordNet BIBREF0 and Freebase BIBREF1 in this paper. Specifically, we use WN18 (a subset of WordNet) BIBREF24 and FB15K (a subset of Freebase) BIBREF2 since their text descriptions are easily publicly available. Table 1 lists statistics of the two datasets.
answer:
What datasets are used to evaluate this paper?


question:
In order to provide directions for future work, we analyze the errors made by the classifier trained on the extended features on the four prediction tasks.

Errors on Intention (I) prediction: The lack of background is a major problem when identifying trolling comments. Non-cursing aggressions and insults This is a challenging problem, since the majority of abusive and insulting comments rely on profanity and swearing.  Another source of error is the presence of controversial topic words such as “black”,“feminism”, “killing”, “racism”, “brown”, etc. that are commonly used by trolls. Errors on Disclosure (D) prediction: A major source of error that affects disclosure is the shallow meaning representation obtained from the BOW model even when augmented with the distributional features given by the glove vectors. Errors on Interpretation (R) prediction: it is a common practice from many users to directly ask the suspected troll if he/she is trolling or not.  Errors on Response Strategy (B) prediction: In some cases there is a blurry line between “Frustrate” and “Neutralize”.  Another challenging problem is the distinction between the classes “Troll” and “Engage”. 
answer:

What is an example of a difficult-to-classify case?