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All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year Sort by creator [A → Z]' creator [Z → A]' publishing year ↑ (asc)' publishing year ↓ (desc)' title [A → Z]' title [Z → A]' Simple Search Hits 1 – 10 of 10 1 Decoding Methods in Neural Language Generation: A Survey Zarrieß, Sina; Schüz, Simeon; Voigt, Henrik. - : MDPI AG, 2021 Abstract: Zarrieß S, Voigt H, Schüz S. Decoding Methods in Neural Language Generation: A Survey. Information . 2021;12(9): 355. ; Neural encoder-decoder models for language generation can be trained to predict words directly from linguistic or non-linguistic inputs. When generating with these so-called end-to-end models, however, the NLG system needs an additional decoding procedure that determines the output sequence, given the infinite search space over potential sequences that could be generated with the given vocabulary. This survey paper provides an overview of the different ways of implementing decoding on top of neural network-based generation models. Research into decoding has become a real trend in the area of neural language generation, and numerous recent papers have shown that the choice of decoding method has a considerable impact on the quality and various linguistic properties of the generation output of a neural NLG system. This survey aims to contribute to a more systematic understanding of decoding methods across different areas of neural NLG. We group the reviewed methods with respect to the broad type of objective that they optimize in the generation of the sequence—likelihood, diversity, and task-specific linguistic constraints or goals—and discuss their respective strengths and weaknesses. Keyword: beam search; ddc:410; decoding; diversity; neural language generation; sampling URL: https://nbn-resolving.org/urn:nbn:de:0070-pub-29570917 https://pub.uni-bielefeld.de/download/2957091/2957092 https://pub.uni-bielefeld.de/record/2957091 BASE Hide details 2 Know What You Don't Know: Modeling a Pragmatic Speaker that Refers to Objects of Unknown Categories ... Zarrieß, Sina; Schlangen, David. - : arXiv, 2019 BASE Show details 3 MeetUp! A Corpus of Joint Activity Dialogues in a Visual Environment ... Ilinykh, Nikolai; Zarrieß, Sina; Schlangen, David. - : arXiv, 2019 BASE Show details 4 The greennn tree - lengthening position influences uncertainty perception Betz, Simon; Zarrieß, Sina; Székely, Éva. - : KTH, Tal, musik och hörsel, TMH, 2019 BASE Show details 5 Meet Up! A Corpus of Joint Activity Dialogues in a Visual Environment Ilinykh, Nikolai; Zarrieß, Sina; Schlangen, David. - : SEMDIAL, 2019 BASE Show details 6 Syntactic and referential choice in corpus-based generation : modeling source, context and interactions Zarrieß, Sina [Verfasser]; Kuhn, Jonas [Akademischer Betreuer]. - Stuttgart : Universitätsbibliothek der Universität Stuttgart, 2016 DNB Subject Category Language Show details 7 Finite-state rule deduction for parsing non-constituent coordination Zarrieß, Sina [Verfasser]; Seeker, Wolfgang [Verfasser]. - Potsdam : Universität Potsdam, 2008 DNB Subject Category Language Show details 8 Finite-state rule deduction for parsing non-constituent coordination Zarrieß, Sina; Seeker, Wolfgang. - 2008 BASE Show details 9 Object naming in language and vision: a survey and a new dataset Silberer, Carina; Zarrieß, Sina; Boleda, Gemma. - : ACL (Association for Computational Linguistics) BASE Show details 10 Humans meet models on object naming: a new dataset and analysis Silberer, Carina; Westera, Matthijs; Boleda, Gemma. - : ACL (Association for Computational Linguistics) BASE Show details

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