# How to pass input to deep learning models for Multiple choice question answering problem?

I'm currently working on a multiple-choice question answering system. The training set consists of a question, answer and 4 options and I need to predict the correct answer among 4 options. Sometimes there is one paragraph too, For example :

1.Which among the following is measured using a Vernier Caliper?

[A] Dimensions
[B] Time
[C] Sound
[D] Temperature

Chapter text: [Book chapter related to Dimension, time, sound and temperature ]


How to feed this input to any of deep learning models? I thought two approaches :

1. Using tokens

and correct and as one hot encoding => [1, 0, 0, 0 ]

1. Using concatenation

Generating fix sized word embedding for each text :

 - Chapter text = [1,1024]
- Text         = [1,1024]
- option_a     = [1,1024]
- option_b     = [1,1024]
- option_c     = [1,1024]
- option_d     = [1,1024]


final_input = concat( [ Chapter text, Text, option_a, option_b, option_c, option_d] ) ==> [1,6144]

and correct and as one hot encoding => [1, 0, 0, 0 ]


Is it good representation for understanding and reasoning over text for mcqa task?