Toward Data-Driven Tutorial Question Answering with Deep Learning Conversational Models

Mayank Kulkarni, Kristy Boyer


Abstract
There has been an increase in popularity of data-driven question answering systems given their recent success. This pa-per explores the possibility of building a tutorial question answering system for Java programming from data sampled from a community-based question answering forum. This paper reports on the creation of a dataset that could support building such a tutorial question answering system and discusses the methodology to create the 106,386 question strong dataset. We investigate how retrieval-based and generative models perform on the given dataset. The work also investigates the usefulness of using hybrid approaches such as combining retrieval-based and generative models. The results indicate that building data-driven tutorial systems using community-based question answering forums holds significant promise.
Anthology ID:
W18-0532
Volume:
Proceedings of the Thirteenth Workshop on Innovative Use of NLP for Building Educational Applications
Month:
June
Year:
2018
Address:
New Orleans, Louisiana
Venues:
BEA | NAACL | WS
SIG:
SIGEDU
Publisher:
Association for Computational Linguistics
Note:
Pages:
273–283
Language:
URL:
https://www.aclweb.org/anthology/W18-0532
DOI:
10.18653/v1/W18-0532
Bib Export formats:
BibTeX MODS XML EndNote
PDF:
http://aclanthology.lst.uni-saarland.de/W18-0532.pdf