Bi-directional Answer-to-Answer Co-attention for Short Answer Grading using Deep Learning
Abstract
So far different research works have been conducted to achieve short answer questions. Hence, due to the advancement of artificial intelligence and adaptability of deep learning models, we introduced a new model to score short answer subjective questions. Using bi-directional answer to answer co-attention, we have demonstrated the extent to which each words and sentences features of student answer detected by the model and shown prom-ising result on both Kaggle and Mohler’s dataset. The experiment on Amharic short an-swer dataset prepared for this research work also shows promising result that can be used as baseline for subsequent works.- Anthology ID:
- 2020.winlp-1.8
- Volume:
- Proceedings of the Fourth Widening Natural Language Processing Workshop
- Month:
- July
- Year:
- 2020
- Address:
- Seattle, USA
- Editors:
- Rossana Cunha, Samira Shaikh, Erika Varis, Ryan Georgi, Alicia Tsai, Antonios Anastasopoulos, Khyathi Raghavi Chandu
- Venue:
- WiNLP
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 26–30
- Language:
- URL:
- https://aclanthology.org/2020.winlp-1.8
- DOI:
- 10.18653/v1/2020.winlp-1.8
- Cite (ACL):
- Abebawu Eshetu, Getenesh Teshome, and Ribka Alemahu. 2020. Bi-directional Answer-to-Answer Co-attention for Short Answer Grading using Deep Learning. In Proceedings of the Fourth Widening Natural Language Processing Workshop, pages 26–30, Seattle, USA. Association for Computational Linguistics.
- Cite (Informal):
- Bi-directional Answer-to-Answer Co-attention for Short Answer Grading using Deep Learning (Eshetu et al., WiNLP 2020)