In this task, you will be presented with a context from an academic paper and a question separated with a
. You have to answer the question based on the context.

[EX Q]: In this paper, we introduce automatic 'drunk-texting prediction' as a computational task. Given a tweet, the goal is to automatically identify if it was written by a drunk user.
Question: Do the authors equate drunk tweeting with drunk texting?
[EX A]: Yes

[EX Q]: A few sample advice rules in English (these are converted to first-order logic format and given as input to our algorithm) are presented in Table TABREF11 . We modified the work of Odom et al. odomAIME15,odomAAAI15 to learn RDNs in the presence of advice. The key idea is to explicitly represent advice in calculating gradients.
Question: How do they incorporate human advice?
[EX A]: by converting human advice to first-order logic format and use as an input to calculate gradient

[EX Q]: Future work can be improving the sensationalism scorer and investigating the applications of dynamic balancing methods between RL and MLE in textGANBIBREF23. Our work also raises the ethical questions about generating sensational headlines, which can be further explored.
Question: What is future work planed?
[EX A]:
ethical questions about generating sensational headlines, which can be further explored improving the sensationalism scorer investigating the applications of dynamic balancing methods between RL and MLE