instruction:
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.
question:
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? 
answer:
Yes


question:
Currently, the following WSD models induced from a text corpus are available: Word senses based on cluster word features. This model uses the cluster words from the induced word sense inventory as sparse features that represent the sense.

Word senses based on context word features. This representation is based on a sum of word vectors of all cluster words in the induced sense inventory weighted by distributional similarity scores.

Super senses based on cluster word features. To build this model, induced word senses are first globally clustered using the Chinese Whispers graph clustering algorithm BIBREF9 . The edges in this sense graph are established by disambiguation of the related words BIBREF11 , BIBREF12 . The resulting clusters represent semantic classes grouping words sharing a common hypernym, e.g. “animal”. This set of semantic classes is used as an automatically learned inventory of super senses: There is only one global sense inventory shared among all words in contrast to the two previous traditional “per word” models. Each semantic class is labeled with hypernyms. This model uses words belonging to the semantic class as features.

Super senses based on context word features. This model relies on the same semantic classes as the previous one but, instead, sense representations are obtained by averaging vectors of words sharing the same class. 
 Question: Do they use a neural model for their task?
answer:
No


question:
The E2E NLG challenge dataset BIBREF21 is utilized in our experiments, which is a crowd-sourced dataset of 50k instances in the restaurant domain. Our models are trained on the official training set and verified on the official testing set.  
 Question: What datasets did they use?
answer:

The E2E NLG challenge dataset BIBREF21