An Approach to Evaluating Subjective Answers using BERT model

The state of art model for language translation, conversion from hand written to digital text, transcription are succeeded in wide range of fields using Natural Language Processing, Artificial Intelligence and Machine Learning (AIML) applications. In present, evaluation of subjective answers are not exercised systematically and graded using computer system. In this work, a mathematical method is proposed for evaluating subjective answers using Bidirectional Encoder Representation Transformers for word embedding and convert the sentence into vector space using pooling method for representing similar sentences. The proposed method evaluates the subjective answers having semantic meaning of answers based on topic Engineering and Medical related questions and answers dataset. It achieves to understand the similarity of different answers which are same semantically. The BERT model is used with machine learning methods to transform the sentence into vector space. The vector space is used to calculate percentage of similarity. The similarity of the sentences with percentage is observed and evaluated.