Automated Answer Validation using Text Similarity
Balaji Ganesan, Arjun Ravikumar, Lakshay Piplani, Rini Bhaumik, Dhivya Padmanaban, Shwetha Narasimhamurthy, Chetan Adhikary, Subhash Deshapogu
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:
- Jyoti D. Pawar, Sobha Lalitha Devi
- Venue:
- ICON
- SIG:
- SIGLEX
- Publisher:
- NLP Association of India (NLPAI)
- Note:
- Pages:
- 807–814
- Language:
- URL:
- https://aclanthology.org/2023.icon-1.83
- DOI:
- Cite (ACL):
- Balaji Ganesan, Arjun Ravikumar, Lakshay Piplani, Rini Bhaumik, Dhivya Padmanaban, Shwetha Narasimhamurthy, Chetan Adhikary, and Subhash Deshapogu. 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 (Ganesan et al., ICON 2023)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-4/2023.icon-1.83.pdf