Encoder-Decoder Architectures for Generating Questions
With exploding textual data on the internet with e-books, legal documents and products information, it is an opportunity to harness it for applications which can aid human tasks. Developing systems for question generation can be used for making frequently-asked-questions, creating school quiz-es and...
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| Published in: | Procedia computer science Vol. 132; pp. 1041 - 1048 |
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| Main Authors: | , |
| Format: | Journal Article |
| Language: | English |
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Elsevier B.V
2018
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| ISSN: | 1877-0509, 1877-0509 |
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| Abstract | With exploding textual data on the internet with e-books, legal documents and products information, it is an opportunity to harness it for applications which can aid human tasks. Developing systems for question generation can be used for making frequently-asked-questions, creating school quiz-es and serve for the purpose of unified AI. Here in this study various encoder decoder architectures for generating questions from text inputs have been explored using Stanford’s SQuAD dataset as for training development and test sets and evaluation metrics such as BLEU, ROUGUE and training time were used to compare the effectiveness of the models. The article develops upon the work of current end-to-end system by using gated recurrent unit in place of long short term memory which give similar accuracy but with lesser training time, further it also show the successfully use of a convolution based encoder for this task which gives results comparable to current state of the art system with much lesser training time. |
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| AbstractList | With exploding textual data on the internet with e-books, legal documents and products information, it is an opportunity to harness it for applications which can aid human tasks. Developing systems for question generation can be used for making frequently-asked-questions, creating school quiz-es and serve for the purpose of unified AI. Here in this study various encoder decoder architectures for generating questions from text inputs have been explored using Stanford’s SQuAD dataset as for training development and test sets and evaluation metrics such as BLEU, ROUGUE and training time were used to compare the effectiveness of the models. The article develops upon the work of current end-to-end system by using gated recurrent unit in place of long short term memory which give similar accuracy but with lesser training time, further it also show the successfully use of a convolution based encoder for this task which gives results comparable to current state of the art system with much lesser training time. |
| Author | Singh, Jaspreet Sharma, Yashvardhan |
| Author_xml | – sequence: 1 givenname: Jaspreet surname: Singh fullname: Singh, Jaspreet email: f2014152@pilani.bits-pilani.ac.in organization: Department of Computer Science Information System Birla Institute of Technology Science, Pilani Campus Pilani-333031 – sequence: 2 givenname: Yashvardhan surname: Sharma fullname: Sharma, Yashvardhan organization: Department of Computer Science Information System Birla Institute of Technology Science, Pilani Campus Pilani-333031 |
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| Keywords | Neural Networks Language Generation Automatic Question Generation Natural Language Processing |
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