Multiple Domain Question-Answer Generation

Kimberly Lu

EECS Department
University of California, Berkeley
Technical Report No. UCB/EECS-2019-44
May 15, 2019

http://www2.eecs.berkeley.edu/Pubs/TechRpts/2019/EECS-2019-44.pdf

In this work we explore the usefulness and practicality of domain adaptation and multi-domain learning methods in question-answer generation. Unlike recent work in question-answer generation which focuses on processing single-domain data to create synthetic reading comprehension datasets (Du and Cardie, 2018), we propose a question-answer generation system that can adapt to datasets containing multiple domains while still achieving similar or better performance in single domains compared to a baseline. We apply our system, consisting of an answer extraction system and a question generation system, to the SQuAD and SciQ reading comprehension datasets and evaluate its efficacy in mixed- and single-domain settings. Our domain adaptation method achieves higher performance than baselines on the mixed-domain and SciQ datasets in both answer extraction and question generation.

Advisor: Dawn Song


BibTeX citation:

@mastersthesis{Lu:EECS-2019-44,
    Author = {Lu, Kimberly},
    Title = {Multiple Domain Question-Answer Generation},
    School = {EECS Department, University of California, Berkeley},
    Year = {2019},
    Month = {May},
    URL = {http://www2.eecs.berkeley.edu/Pubs/TechRpts/2019/EECS-2019-44.html},
    Number = {UCB/EECS-2019-44},
    Abstract = {In this work we explore the usefulness and practicality of domain adaptation and multi-domain learning methods in question-answer generation. Unlike recent work in question-answer generation which focuses on processing single-domain data to create synthetic reading comprehension datasets (Du and Cardie, 2018), we propose a question-answer generation system that can adapt to datasets containing multiple domains while still achieving similar or better performance in single domains compared to a baseline. We apply our system, consisting of an answer extraction system and a question generation system, to the SQuAD and SciQ reading comprehension datasets and evaluate its efficacy in mixed- and single-domain settings. Our domain adaptation method achieves higher performance than baselines on the mixed-domain and SciQ datasets in both answer extraction and question generation.}
}

EndNote citation:

%0 Thesis
%A Lu, Kimberly
%T Multiple Domain Question-Answer Generation
%I EECS Department, University of California, Berkeley
%D 2019
%8 May 15
%@ UCB/EECS-2019-44
%U http://www2.eecs.berkeley.edu/Pubs/TechRpts/2019/EECS-2019-44.html
%F Lu:EECS-2019-44