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.

[Q]: We experiment with two image captioning models: the Show&Tell model BIBREF0 and the LRCN1u model BIBREF1. Both models follow the basic encoder-decoder architecture design that uses a CNN encoder to condense the visual information into an image embedding, which in turn conditions an LSTM decoder to generate a natural language caption. The main difference between the two models is the way they condition the decoder. The Show&Tell model feeds the image embedding as the “predecessor word embedding” to the first produced word, while the LRCN1u model concatenates the image features with the embedded previous word as the input to the sequence model at each time step. 
 Question: Which existing models are evaluated?
[A]: Show&Tell and LRCN1u


[Q]: To account for sequences of words and characters that might carry useful information, we extracted word unigrams, bigrams, and trigrams as features. 
 Question: Which features do they use to model Twitter messages?
[A]: word unigrams, bigrams, and trigrams


[Q]: As shown in Table , the proposed PS-rnn-elmo shows a significant MAP performance improvement compared to the previous best model, CompClip-LM (0.696 to 0.734 absolute). 
 Question: How much better performance of proposed model compared to answer-selection models?
[A]:
significant MAP performance improvement compared to the previous best model, CompClip-LM (0.696 to 0.734 absolute)