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.

The base sentence embedding model is a BiLSTM over all words in the respective sequences with randomly initialised word embeddings, following BIBREF30 . We opt for this strong baseline sentence encoding model, as opposed to engineering sentence embeddings that work particularly well for this dataset, to showcase the dataset. We would expect pre-trained contextual encoding models, e.g. ELMO BIBREF33 , ULMFit BIBREF34 , BERT BIBREF35 , to offer complementary performance gains, as has been shown for a few recent papers BIBREF36 , BIBREF37 . 
 Question: What were the baselines?
a BiLSTM over all words in the respective sequences with randomly initialised word embeddings, following BIBREF30

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? 
Yes

The following handcrafted features were used for the model:

Bias feature

Token feature

Uppercase feature (y/n)

Titlecase feature (y/n)

Character trigram feature

Quotation feature (y/n)

Word suffix feature (last three characters)

POS tag (provided by spaCy utilities)

Word shape (provided by spaCy utilities)

Word embedding (see Table TABREF26) 
 Question: What are the handcrafted features used?

Bias feature Token feature Uppercase feature (y/n) Titlecase feature (y/n) Character trigram feature Quotation feature (y/n) Word suffix feature (last three characters) POS tag (provided by spaCy utilities) Word shape (provided by spaCy utilities) Word embedding (see Table TABREF26)