Ribka Alemahu


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2020

bib
Bi-directional Answer-to-Answer Co-attention for Short Answer Grading using Deep Learning
Abebawu Eshetu | Getenesh Teshome | Ribka Alemahu
Proceedings of the Fourth Widening Natural Language Processing Workshop

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.