Automated Answer Validation using Text Similarity

Ganesan Balaji, Ravikumar Arjun, Piplani Lakshay, Bhaumik Rini, Padmanaban Dhivya, Narasimhamurthy Shwetha, Adhikary Chetan, Deshapogu Subhash


Abstract
Automated answer validation can help improve learning outcomes by providing appropriate feedback to learners, and by making question answering systems and online learning solutions more widely available. There have been some works in science question answering which show that information retrieval methods outperform neural methods, especially in the multiple choice version of this problem. We implement Siamese neural network models and produce a generalised solution to this problem. We compare our supervised model with other text similarity based solutions.
Anthology ID:
2023.icon-1.83
Volume:
Proceedings of the 20th International Conference on Natural Language Processing (ICON)
Month:
December
Year:
2023
Address:
Goa University, Goa, India
Editors:
D. Pawar Jyoti, Lalitha Devi Sobha
Venue:
ICON
SIG:
SIGLEX
Publisher:
NLP Association of India (NLPAI)
Note:
Pages:
807–814
Language:
URL:
https://aclanthology.org/2023.icon-1.83
DOI:
Bibkey:
Cite (ACL):
Ganesan Balaji, Ravikumar Arjun, Piplani Lakshay, Bhaumik Rini, Padmanaban Dhivya, Narasimhamurthy Shwetha, Adhikary Chetan, and Deshapogu Subhash. 2023. Automated Answer Validation using Text Similarity. In Proceedings of the 20th International Conference on Natural Language Processing (ICON), pages 807–814, Goa University, Goa, India. NLP Association of India (NLPAI).
Cite (Informal):
Automated Answer Validation using Text Similarity (Balaji et al., ICON 2023)
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PDF:
https://aclanthology.org/2023.icon-1.83.pdf