TASK DEFINITION: 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.
PROBLEM: The seq2seq model with global attention gives the best results with an average target BLEU score of 29.65 on the style transfer dataset, compared with an average target BLEU score of 26.97 using the seq2seq model with pointer networks. 
 Question: What is best BLEU score of language style transfer authors got?

SOLUTION: seq2seq model with global attention gives the best results with an average target BLEU score of 29.65

PROBLEM: We train a single layer LSTM with a 150-dimensional hidden state for hate / not hate classification. The input dimensionality is set to 100 and GloVe BIBREF26 embeddings are used as word input representations. 
 Question: What unimodal detection models were used?

SOLUTION:  single layer LSTM with a 150-dimensional hidden state for hate / not hate classification

PROBLEM: Since the input of our method is textual data, we follow the approach of BIBREF15 and map the text into a fixed-size vector representation. To this end, we use word embeddings that were successfully applied in other domains. We follow BIBREF5 and use pre-trained GloVe word vectors BIBREF16 to initialize the embedding layer (also known as look-up table). Section SECREF18 discusses the embedding layer in more details. 
 Question: Which pretrained word vectors did they use?

SOLUTION:
 pre-trained GloVe word vectors