@inproceedings{eshetu-etal-2020-bi,
title = "Bi-directional Answer-to-Answer Co-attention for Short Answer Grading using Deep Learning",
author = "Eshetu, Abebawu and
Teshome, Getenesh and
Alemahu, Ribka",
editor = "Cunha, Rossana and
Shaikh, Samira and
Varis, Erika and
Georgi, Ryan and
Tsai, Alicia and
Anastasopoulos, Antonios and
Chandu, Khyathi Raghavi",
booktitle = "Proceedings of the Fourth Widening Natural Language Processing Workshop",
month = jul,
year = "2020",
address = "Seattle, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.winlp-1.8",
doi = "10.18653/v1/2020.winlp-1.8",
pages = "26--30",
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.",
}
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%0 Conference Proceedings
%T Bi-directional Answer-to-Answer Co-attention for Short Answer Grading using Deep Learning
%A Eshetu, Abebawu
%A Teshome, Getenesh
%A Alemahu, Ribka
%Y Cunha, Rossana
%Y Shaikh, Samira
%Y Varis, Erika
%Y Georgi, Ryan
%Y Tsai, Alicia
%Y Anastasopoulos, Antonios
%Y Chandu, Khyathi Raghavi
%S Proceedings of the Fourth Widening Natural Language Processing Workshop
%D 2020
%8 July
%I Association for Computational Linguistics
%C Seattle, USA
%F eshetu-etal-2020-bi
%X 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.
%R 10.18653/v1/2020.winlp-1.8
%U https://aclanthology.org/2020.winlp-1.8
%U https://doi.org/10.18653/v1/2020.winlp-1.8
%P 26-30
Markdown (Informal)
[Bi-directional Answer-to-Answer Co-attention for Short Answer Grading using Deep Learning](https://aclanthology.org/2020.winlp-1.8) (Eshetu et al., WiNLP 2020)
ACL