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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| Veröffentlicht in: | Procedia computer science Jg. 132; S. 1041 - 1048 |
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2018
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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 |
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| Keywords | Neural Networks Language Generation Automatic Question Generation Natural Language Processing |
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| References_xml | – reference: Rus, V., Wyse, B., Piwek, P., Lintean, M., Stoyanchev, S., Moldovan, C. (2010, July). The first question generation shared task evaluation challenge. In Proceedings of the 6th International Natural Language Generation Conference (pp. 251-257). Association for Computational Linguistics. – reference: Lin, C.Y., & Och, F.J. (2004, July). Automatic evaluation of machine translation quality using longest common subsequence and skip-bigram statistics. In Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics (p. 605). 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