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
Editors:
Joel Tetreault, Jill Burstein, Ekaterina Kochmar, Claudia Leacock, Helen Yannakoudakis
Venue:
BEA
SIG:
SIGEDU
Publisher:
Association for Computational Linguistics
Note:
Pages:
273–283
Language:
URL:
https://aclanthology.org/W18-0532
DOI:
10.18653/v1/W18-0532
Bibkey:
Cite (ACL):
Mayank Kulkarni and Kristy Boyer. 2018. Toward Data-Driven Tutorial Question Answering with Deep Learning Conversational Models. In Proceedings of the Thirteenth Workshop on Innovative Use of NLP for Building Educational Applications, pages 273–283, New Orleans, Louisiana. Association for Computational Linguistics.
Cite (Informal):
Toward Data-Driven Tutorial Question Answering with Deep Learning Conversational Models (Kulkarni & Boyer, BEA 2018)
Copy Citation:
PDF:
https://preview.aclanthology.org/nschneid-patch-1/W18-0532.pdf