Matches in SemOpenAlex for { <https://semopenalex.org/work/W3215537108> ?p ?o ?g. }
- W3215537108 abstract "Driven by the increasingly larger deep learning models, neural languagegeneration (NLG) has enjoyed unprecedentedly improvement and is now able togenerate a diversity of human-like texts on demand, granting itself thecapability of serving as a human writing assistant. Text attribute transfer isone of the most important NLG tasks, which aims to control certain attributesthat people may expect the texts to possess, such as sentiment, tense, emotion,political position, etc. It has a long history in Natural Language Processingbut recently gains much more attention thanks to the promising performancebrought by deep learning models. In this article, we present a systematicsurvey on these works for neural text attribute transfer. We collect allrelated academic works since the first appearance in 2017. We then select,summarize, discuss, and analyze around 65 representative works in acomprehensive way. Overall, we have covered the task formulation, existingdatasets and metrics for model development and evaluation, and all methodsdeveloped over the last several years. We reveal that existing methods areindeed based on a combination of several loss functions with each of whichserving a certain goal. Such a unique perspective we provide could shed lighton the design of new methods. We conclude our survey with a discussion on openissues that need to be resolved for better future development." @default.
- W3215537108 created "2021-12-06" @default.
- W3215537108 creator A5016724158 @default.
- W3215537108 creator A5069960741 @default.
- W3215537108 creator A5082450455 @default.
- W3215537108 date "2020-11-01" @default.
- W3215537108 modified "2023-09-23" @default.
- W3215537108 title "Deep Learning for Text Attribute Transfer: A Survey" @default.
- W3215537108 cites W1231128075 @default.
- W3215537108 cites W2034742893 @default.
- W3215537108 cites W2068511562 @default.
- W3215537108 cites W2099471712 @default.
- W3215537108 cites W2101105183 @default.
- W3215537108 cites W2108325777 @default.
- W3215537108 cites W2117278770 @default.
- W3215537108 cites W2123301721 @default.
- W3215537108 cites W2125101937 @default.
- W3215537108 cites W2130942839 @default.
- W3215537108 cites W2144578941 @default.
- W3215537108 cites W2145094598 @default.
- W3215537108 cites W2250342921 @default.
- W3215537108 cites W2525332836 @default.
- W3215537108 cites W2547875792 @default.
- W3215537108 cites W2553897675 @default.
- W3215537108 cites W2604799547 @default.
- W3215537108 cites W2625940279 @default.
- W3215537108 cites W2740094762 @default.
- W3215537108 cites W2889056219 @default.
- W3215537108 cites W2890397703 @default.
- W3215537108 cites W2891177506 @default.
- W3215537108 cites W2891348164 @default.
- W3215537108 cites W2898410387 @default.
- W3215537108 cites W2925000324 @default.
- W3215537108 cites W2932618389 @default.
- W3215537108 cites W2933374552 @default.
- W3215537108 cites W2949830548 @default.
- W3215537108 cites W2950692458 @default.
- W3215537108 cites W2952317054 @default.
- W3215537108 cites W2962731009 @default.
- W3215537108 cites W2962753250 @default.
- W3215537108 cites W2962793481 @default.
- W3215537108 cites W2962801832 @default.
- W3215537108 cites W2962912551 @default.
- W3215537108 cites W2963034998 @default.
- W3215537108 cites W2963206679 @default.
- W3215537108 cites W2963216553 @default.
- W3215537108 cites W2963241138 @default.
- W3215537108 cites W2963352809 @default.
- W3215537108 cites W2963366196 @default.
- W3215537108 cites W2963667126 @default.
- W3215537108 cites W2963748441 @default.
- W3215537108 cites W2963796896 @default.
- W3215537108 cites W2963804993 @default.
- W3215537108 cites W2963829526 @default.
- W3215537108 cites W2963929190 @default.
- W3215537108 cites W2964008635 @default.
- W3215537108 cites W2964165364 @default.
- W3215537108 cites W2964222296 @default.
- W3215537108 cites W2964308564 @default.
- W3215537108 cites W2964321064 @default.
- W3215537108 cites W2965033324 @default.
- W3215537108 cites W2970562804 @default.
- W3215537108 cites W2970901330 @default.
- W3215537108 cites W2971232986 @default.
- W3215537108 cites W2996403597 @default.
- W3215537108 cites W2996851481 @default.
- W3215537108 cites W2997243793 @default.
- W3215537108 cites W3015482531 @default.
- W3215537108 cites W3035125262 @default.
- W3215537108 cites W3035352643 @default.
- W3215537108 cites W3035376412 @default.
- W3215537108 cites W3098649723 @default.
- W3215537108 cites W3113837855 @default.
- W3215537108 cites W3122709690 @default.
- W3215537108 cites W411382560 @default.
- W3215537108 cites W658020064 @default.
- W3215537108 hasPublicationYear "2020" @default.
- W3215537108 type Work @default.
- W3215537108 sameAs 3215537108 @default.
- W3215537108 citedByCount "0" @default.
- W3215537108 crossrefType "posted-content" @default.
- W3215537108 hasAuthorship W3215537108A5016724158 @default.
- W3215537108 hasAuthorship W3215537108A5069960741 @default.
- W3215537108 hasAuthorship W3215537108A5082450455 @default.
- W3215537108 hasConcept C108583219 @default.
- W3215537108 hasConcept C119857082 @default.
- W3215537108 hasConcept C12713177 @default.
- W3215537108 hasConcept C144024400 @default.
- W3215537108 hasConcept C150899416 @default.
- W3215537108 hasConcept C154945302 @default.
- W3215537108 hasConcept C162324750 @default.
- W3215537108 hasConcept C187736073 @default.
- W3215537108 hasConcept C19165224 @default.
- W3215537108 hasConcept C204321447 @default.
- W3215537108 hasConcept C2522767166 @default.
- W3215537108 hasConcept C2780451532 @default.
- W3215537108 hasConcept C2781316041 @default.
- W3215537108 hasConcept C41008148 @default.
- W3215537108 hasConceptScore W3215537108C108583219 @default.
- W3215537108 hasConceptScore W3215537108C119857082 @default.