SYSTEM AND METHOD FOR APPLYING ARTIFICIAL INTELLIGENCE TECHNIQUES TO RESPOND TO MULTIPLE CHOICE QUESTIONS
A system for answering multiple choice questions includes at least one processor configured to create a question answering model using a training data set. The system is configured to create a balanced data from the imbalanced training data set. The balancing of the imbalanced training data set is achieved by generating synthetic instances of at least one minority category, among a plurality of categories into which the training data set is categorized.
1 . A system for answering multiple choice questions, the system comprising at least one processor configured to create a question answering model, wherein,
imbalance in a training data set is balanced; and
balancing of the training data set is achieved by generating synthetic instances of at least one minority category, among a plurality of categories into which the training data set is categorized.
2 . The system according to claim 1 , wherein the processor is configured to generate the synthetic instances of the at least one minority category using Synthetic Minority Oversampling Technique (SMOTE).
3 . The system according to claim 2 , wherein the synthetic instances are generated using the formula:
x new =x+r ( x nn −x )
wherein,
“x” is an instance in the minority category;
“x new ” is a synthetic instance in the minority category;
“x nn ” is an instance in the minority category neighbouring the instance “x”; and
“r” is a number between 0 and 1.
4 . The system according to claim 1 , wherein the processor is further configured to pass a generated data set, which is obtained by balancing of the training data set, to a classification algorithm to create the question answering model, wherein the classification algorithm learns a mapping as:
answer= f (evidence,question).
5 . The system according to claim 4 , wherein the classification algorithm is logistic regression.
6 . The system according to claim 1 , wherein the processor is configured to answer multiple choice questions using the question answering model.
7 . A method for answering multiple choice questions, the method comprising creating a question answering model, wherein question answering model is created by balancing imbalance present in a training data, wherein balancing of the training data set is achieved by generating synthetic instances of at least one minority category, among a plurality of categories into which the training data set is categorized.
8 . The method according to claim 7 , wherein the synthetic instances of the at least one minority category are generated using Synthetic Minority Oversampling TEchnique (SMOTE).
9 . The method according to claim 8 , wherein the synthetic instances are generated using the formula:
x new =x+r ( x nn −x )
wherein,
“x” is an instance in the minority category;
“x new ” is a synthetic instance in the minority category;
“x nn ” is an instance in the minority category neighbouring the instance “x”; and
“r” is a number between 0 and 1.
10 . The method according to claim 7 , further comprising passing a generated data set, which is obtained by balancing of the training data set, to a classification algorithm to create the question answering model, wherein the classification algorithm learns a mapping as:
answer= f (evidence;question).
11 . The method according to claim 10 , wherein the classification algorithm is logistic regression.
12 . The method according to claim 7 , further comprising, answering multiple choice questions using the question answering model.
13 . A non-transitory computer readable medium having stored thereon software instructions that, when executed by a processor, cause the processor to create a question answering model, by executing the steps comprising, balancing imbalance present in a training data, wherein balancing of the training data set is achieved by generating synthetic instances of at least one minority category, among a plurality of categories into which the training data set is categorized.