ADAPT Centre Cone Team at IJCNLP-2017 Task 5: A Similarity-Based Logistic Regression Approach to Multi-choice Question Answering in an Examinations Shared Task

Daria Dzendzik, Alberto Poncelas, Carl Vogel, Qun Liu

Abstract
We describe the work of a team from the ADAPT Centre in Ireland in addressing automatic answer selection for the Multi-choice Question Answering in Examinations shared task. The system is based on a logistic regression over the string similarities between question, answer, and additional text. We obtain the highest grade out of six systems: 48.7% accuracy on a validation set (vs. a baseline of 29.45%) and 45.6% on a test set.
Anthology ID:
I17-4010
Volume:
Proceedings of the IJCNLP 2017, Shared Tasks
Month:
December
Year:
2017
Address:
Taipei, Taiwan
Editors:
Chao-Hong Liu, Preslav Nakov, Nianwen Xue
Venue:
IJCNLP
SIG:
Publisher:
Asian Federation of Natural Language Processing
Note:
Pages:
67–72
Language:
URL:
https://aclanthology.org/I17-4010
DOI:
Bibkey:
Cite (ACL):
Daria Dzendzik, Alberto Poncelas, Carl Vogel, and Qun Liu. 2017. ADAPT Centre Cone Team at IJCNLP-2017 Task 5: A Similarity-Based Logistic Regression Approach to Multi-choice Question Answering in an Examinations Shared Task. In Proceedings of the IJCNLP 2017, Shared Tasks, pages 67–72, Taipei, Taiwan. Asian Federation of Natural Language Processing.
Cite (Informal):
ADAPT Centre Cone Team at IJCNLP-2017 Task 5: A Similarity-Based Logistic Regression Approach to Multi-choice Question Answering in an Examinations Shared Task (Dzendzik et al., IJCNLP 2017)
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