Descriptive Answer Script Grading System using CNN-BiLSTM Network
Shirien K A1, Neethu George2, Surekha Mariam Varghese3
1Shirien K A*, Computer Science Department, Mar Athanasius College of Engineering, Kothamangalam, India.
2Neethu George, Computer Science Department, Mar Athanasius College of Engineering, Kothamangalam, India.
3Dr. Surekha Mariam Varghese, Computer Science Department, Mar Athanasius College of Engineering, Kothamangalam, India.
Manuscript received on January 07, 2021. | Revised Manuscript received on January 15, 2021. | Manuscript published on January 30, 2021. | PP: 139-144 | Volume-9 Issue-5, January 2021. | Retrieval Number: 100.1/ijrte.E5212019521 | DOI: 10.35940/ijrte.E5212.019521
Open Access | Ethics and Policies | Cite | Mendeley
© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Descriptive answer script assessment and rating program is an automated framework to evaluate the answer scripts correctly. There are several classification schemes in which a piece of text is evaluated on the basis of spelling, semantics and meaning. But, lots of these aren’t successful. Some of the models available to rate the response scripts include Simple Long Short Term Memory (LSTM), Deep LSTM. In addition to that Convolution Neural Network and Bi-directional LSTM is considered here to refine the result. The model uses convolutional neural networks and bidirectional LSTM networks to learn local information of words and capture long-term dependency information of contexts on the Tensorflow and Keras deep learning framework. The embedding semantic representation of texts can be used for computing semantic similarities between pieces of texts and to grade them based on the similarity score. The experiment used methods for data optimization, such as data normalization and dropout, and tested the model on an Automated Student Evaluation Short Response Scoring, a commonly used public dataset. By comparing with the existing systems, the proposed model has achieved the state-of-the-art performance and achieves better results in the accuracy of the test dataset.
Keywords: Artificial Intelligence, Convolution Neural Networks, LSTM Networks, Machine Learning.