Integrating Question Classification and Deep Learning for improved Answer Selection

Harish Tayyar Madabushi, Mark Lee, John Barnden


Abstract
We present a system for Answer Selection that integrates fine-grained Question Classification with a Deep Learning model designed for Answer Selection. We detail the necessary changes to the Question Classification taxonomy and system, the creation of a new Entity Identification system and methods of highlighting entities to achieve this objective. Our experiments show that Question Classes are a strong signal to Deep Learning models for Answer Selection, and enable us to outperform the current state of the art in all variations of our experiments except one. In the best configuration, our MRR and MAP scores outperform the current state of the art by between 3 and 5 points on both versions of the TREC Answer Selection test set, a standard dataset for this task.
Anthology ID:
C18-1278
Volume:
Proceedings of the 27th International Conference on Computational Linguistics
Month:
August
Year:
2018
Address:
Santa Fe, New Mexico, USA
Venue:
COLING
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
3283–3294
Language:
URL:
https://www.aclweb.org/anthology/C18-1278
DOI:
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PDF:
http://aclanthology.lst.uni-saarland.de/C18-1278.pdf