two approaches come to my mind:


for each question, you can create 4 sentence sequences, each blank filled with one of the options.
now you have 4 sentences. label the sentence with the correct option 1 and others 0.
now if you have N questions, you will have 4*N annotated sentences.
train an RNN for this. it should work.


fill in the blanks with the correct options. throw wrong options away.
now you will have many sentences, all correct. now learn you can train any seq2word network.
this way, given a new sentence with a blank on it. you can prioritize the 4 options and say the one with the highest probability is the correct one.