Ask to Learn: A Study on Curiosity-driven Question Generation

Thomas Scialom, Jacopo Staiano


Abstract
We propose a novel text generation task, namely Curiosity-driven Question Generation. We start from the observation that the Question Generation task has traditionally been considered as the dual problem of Question Answering, hence tackling the problem of generating a question given the text that contains its answer. Such questions can be used to evaluate machine reading comprehension. However, in real life, and especially in conversational settings, humans tend to ask questions with the goal of enriching their knowledge and/or clarifying aspects of previously gathered information. We refer to these inquisitive questions as Curiosity-driven: these questions are generated with the goal of obtaining new information (the answer) which is not present in the input text. In this work, we experiment on this new task using a conversational Question Answering (QA) dataset; further, since the majority of QA dataset are not built in a conversational manner, we describe a methodology to derive data for this novel task from non-conversational QA data. We investigate several automated metrics to measure the different properties of Curious Questions, and experiment different approaches on the Curiosity-driven Question Generation task, including model pre-training and reinforcement learning. Finally, we report a qualitative evaluation of the generated outputs.
Anthology ID:
2020.coling-main.202
Volume:
Proceedings of the 28th International Conference on Computational Linguistics
Month:
December
Year:
2020
Address:
Barcelona, Spain (Online)
Venue:
COLING
SIG:
Publisher:
International Committee on Computational Linguistics
Note:
Pages:
2224–2235
Language:
URL:
https://aclanthology.org/2020.coling-main.202
DOI:
10.18653/v1/2020.coling-main.202
Bibkey:
Cite (ACL):
Thomas Scialom and Jacopo Staiano. 2020. Ask to Learn: A Study on Curiosity-driven Question Generation. In Proceedings of the 28th International Conference on Computational Linguistics, pages 2224–2235, Barcelona, Spain (Online). International Committee on Computational Linguistics.
Cite (Informal):
Ask to Learn: A Study on Curiosity-driven Question Generation (Scialom & Staiano, COLING 2020)
Copy Citation:
PDF:
https://aclanthology.org/2020.coling-main.202.pdf
Data
CoQAQuACSQuAD