TY - GEN
T1 - Question generation through transfer learning
AU - Liao, Yin Hsiang
AU - Koh, Jia Ling
N1 - Funding Information:
This research is partially supported by the “Aim for the Top University Project” of National Taiwan Normal University (NTNU), sponsored by the Ministry of Education and Ministry of Science and Technology, Taiwan, R.O.C. under Grant no. MOST 108-2221-E-003-010.
Publisher Copyright:
© Springer Nature Switzerland AG 2020.
PY - 2020
Y1 - 2020
N2 - An automatic question generation (QG) system aims to produce questions from a text, such as a sentence or a paragraph. Traditional approaches are mainly based on heuristic, hand-crafted rules to transduce a declarative sentence to a related interrogative sentence. However, creating such a set of rules requires deep linguistic knowledge and most of these rules are language-specific. Although a data-driven approach reduces the participation of linguistic experts, to get sufficient labeled data for QG model training is still a difficult task. In this paper, we applied a neural sequence-to-sequence pointer-generator network with various transfer learning strategies to capture the underlying information of making a question, on a target domain with rare training pairs. Our experiment demonstrates the viability of domain adaptation in QG task. We also show the possibility that transfer learning is helpful in a semi-supervised approach when the amount of training pairs in the target QG dataset is not large enough.
AB - An automatic question generation (QG) system aims to produce questions from a text, such as a sentence or a paragraph. Traditional approaches are mainly based on heuristic, hand-crafted rules to transduce a declarative sentence to a related interrogative sentence. However, creating such a set of rules requires deep linguistic knowledge and most of these rules are language-specific. Although a data-driven approach reduces the participation of linguistic experts, to get sufficient labeled data for QG model training is still a difficult task. In this paper, we applied a neural sequence-to-sequence pointer-generator network with various transfer learning strategies to capture the underlying information of making a question, on a target domain with rare training pairs. Our experiment demonstrates the viability of domain adaptation in QG task. We also show the possibility that transfer learning is helpful in a semi-supervised approach when the amount of training pairs in the target QG dataset is not large enough.
KW - Question generation
KW - Sequence-to-sequence model
KW - Transfer learning
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U2 - 10.1007/978-3-030-55789-8_1
DO - 10.1007/978-3-030-55789-8_1
M3 - Conference contribution
AN - SCOPUS:85091311433
SN - 9783030557881
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 3
EP - 17
BT - Trends in Artificial Intelligence Theory and Applications. Artificial Intelligence Practices - 33rd International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2020, Proceedings
A2 - Fujita, Hamido
A2 - Sasaki, Jun
A2 - Fournier-Viger, Philippe
A2 - Ali, Moonis
PB - Springer Science and Business Media Deutschland GmbH
T2 - 33rd International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2020
Y2 - 22 September 2020 through 25 September 2020
ER -