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.

Advisors: Dawn Song

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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