Matches in SemOpenAlex for { <https://semopenalex.org/work/W2610891036> ?p ?o ?g. }
- W2610891036 abstract "We propose a recurrent neural model that generates natural-language questions from documents, conditioned on answers. We show how to train the model using a combination of supervised and reinforcement learning. After teacher forcing for standard maximum likelihood training, we fine-tune the model using policy gradient techniques to maximize several rewards that measure question quality. Most notably, one of these rewards is the performance of a question-answering system. We motivate question generation as a means to improve the performance of question answering systems. Our model is trained and evaluated on the recent question-answering dataset SQuAD." @default.
- W2610891036 created "2017-05-12" @default.
- W2610891036 creator A5002700508 @default.
- W2610891036 creator A5013161871 @default.
- W2610891036 creator A5013965909 @default.
- W2610891036 creator A5014123853 @default.
- W2610891036 creator A5041145688 @default.
- W2610891036 creator A5043177488 @default.
- W2610891036 creator A5058417675 @default.
- W2610891036 creator A5072931308 @default.
- W2610891036 date "2017-01-01" @default.
- W2610891036 modified "2023-10-18" @default.
- W2610891036 title "Machine Comprehension by Text-to-Text Neural Question Generation" @default.
- W2610891036 cites W1486649854 @default.
- W2610891036 cites W1518951372 @default.
- W2610891036 cites W1531374185 @default.
- W2610891036 cites W1544827683 @default.
- W2610891036 cites W1828163288 @default.
- W2610891036 cites W2057629119 @default.
- W2610891036 cites W2064675550 @default.
- W2610891036 cites W2095705004 @default.
- W2610891036 cites W2099471712 @default.
- W2610891036 cites W2101105183 @default.
- W2610891036 cites W2140679639 @default.
- W2610891036 cites W2181507835 @default.
- W2610891036 cites W2250425483 @default.
- W2610891036 cites W2250539671 @default.
- W2610891036 cites W2250794458 @default.
- W2610891036 cites W2251056936 @default.
- W2610891036 cites W2283204694 @default.
- W2610891036 cites W2384495648 @default.
- W2610891036 cites W2487501366 @default.
- W2610891036 cites W2507756961 @default.
- W2610891036 cites W2521709538 @default.
- W2610891036 cites W2551396370 @default.
- W2610891036 cites W2557764419 @default.
- W2610891036 cites W2566011400 @default.
- W2610891036 cites W2566627449 @default.
- W2610891036 cites W2589049937 @default.
- W2610891036 cites W2605243085 @default.
- W2610891036 cites W2951534261 @default.
- W2610891036 cites W2951813108 @default.
- W2610891036 cites W2962944953 @default.
- W2610891036 cites W2963019137 @default.
- W2610891036 cites W2963167310 @default.
- W2610891036 cites W2963211300 @default.
- W2610891036 cites W2963248296 @default.
- W2610891036 cites W2963351776 @default.
- W2610891036 cites W2963595025 @default.
- W2610891036 cites W2963748441 @default.
- W2610891036 cites W2963938442 @default.
- W2610891036 cites W2963962369 @default.
- W2610891036 cites W2964121744 @default.
- W2610891036 cites W2964236999 @default.
- W2610891036 cites W2964267515 @default.
- W2610891036 cites W2964308564 @default.
- W2610891036 cites W2964335273 @default.
- W2610891036 cites W803028973 @default.
- W2610891036 doi "https://doi.org/10.18653/v1/w17-2603" @default.
- W2610891036 hasPublicationYear "2017" @default.
- W2610891036 type Work @default.
- W2610891036 sameAs 2610891036 @default.
- W2610891036 citedByCount "116" @default.
- W2610891036 countsByYear W26108910362017 @default.
- W2610891036 countsByYear W26108910362018 @default.
- W2610891036 countsByYear W26108910362019 @default.
- W2610891036 countsByYear W26108910362020 @default.
- W2610891036 countsByYear W26108910362021 @default.
- W2610891036 countsByYear W26108910362022 @default.
- W2610891036 countsByYear W26108910362023 @default.
- W2610891036 crossrefType "proceedings-article" @default.
- W2610891036 hasAuthorship W2610891036A5002700508 @default.
- W2610891036 hasAuthorship W2610891036A5013161871 @default.
- W2610891036 hasAuthorship W2610891036A5013965909 @default.
- W2610891036 hasAuthorship W2610891036A5014123853 @default.
- W2610891036 hasAuthorship W2610891036A5041145688 @default.
- W2610891036 hasAuthorship W2610891036A5043177488 @default.
- W2610891036 hasAuthorship W2610891036A5058417675 @default.
- W2610891036 hasAuthorship W2610891036A5072931308 @default.
- W2610891036 hasBestOaLocation W26108910361 @default.
- W2610891036 hasConcept C111472728 @default.
- W2610891036 hasConcept C119857082 @default.
- W2610891036 hasConcept C124101348 @default.
- W2610891036 hasConcept C127313418 @default.
- W2610891036 hasConcept C137293760 @default.
- W2610891036 hasConcept C138885662 @default.
- W2610891036 hasConcept C154945302 @default.
- W2610891036 hasConcept C195324797 @default.
- W2610891036 hasConcept C197115733 @default.
- W2610891036 hasConcept C199360897 @default.
- W2610891036 hasConcept C204321447 @default.
- W2610891036 hasConcept C2779530757 @default.
- W2610891036 hasConcept C2780009758 @default.
- W2610891036 hasConcept C41008148 @default.
- W2610891036 hasConcept C44291984 @default.
- W2610891036 hasConcept C49204034 @default.
- W2610891036 hasConcept C50644808 @default.
- W2610891036 hasConcept C511192102 @default.
- W2610891036 hasConcept C97541855 @default.
- W2610891036 hasConceptScore W2610891036C111472728 @default.