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Unsupervised quantification of entity consistency between photos and text in real-world news ...
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Müller-Budack, Eric. - : Hannover : Institutionelles Repositorium der Leibniz Universität Hannover, 2022
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Giant Pigeon and Small Person: Prompting Visually Grounded Models about the Size of Objects ...
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Zhang, Yi. - : Purdue University Graduate School, 2022
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Giant Pigeon and Small Person: Prompting Visually Grounded Models about the Size of Objects ...
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Zhang, Yi. - : Purdue University Graduate School, 2022
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CLIL e recursos hipersensoriais personalizados: simbiose perfeita de ensino e aprendizagem de Inglês no 1.º Ciclo do Ensino Básico
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Sentence Level Embedding Detoxification via Toxic Component Removal ...
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: University of Virginia, 2022
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The Effects of Event Depictions in Second Language Phrasal Vocabulary Learning
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Machine Learning approaches for Topic and Sentiment Analysis in multilingual opinions and low-resource languages: From English to Guarani
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Representation learning of natural language and its application to language understanding and generation
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Probing professional identities of English Language Teacher Educators through collaborative study groups: Insights from a teacher educator team in Colombia
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Torres Rocha, JC. - : University of Exeter, 2022. : College of Sicial Sciences and international studies, 2022
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CLIL as an inclusive approach. a didactic proposal to attend to diversity
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Social Media and Intercultural Learning: An approach to EFL for Secondary Students
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Exploration of secondary EFL teachers' and students' perceptions of extensive reading in English and its implementation in Chinese secondary schools: a longitudinal case study
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Phonetic accommodation of human interlocutors in the context of human-computer interaction
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Gessinger, Iona. - : Saarländische Universitäts- und Landesbibliothek, 2022
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Controlled Generation of Stylized Text Using Semantic and Phonetic Representations
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Abstract:
Neural networks are a popular choice of models for the purpose of text generation. Variational autoencoders have been shown to be good at reconstructing text and generating novel text. However, controlling certain aspects of the generated text (e.g., length, semantics, cadence) has proven a more difficult task. The objectives of disentanglement and controlled text generation have thus become areas of interest, with various approaches depending on the aspects we desire to control. In this work we study controllable generation of lyric text based on semantic and phonetic criteria. The phonetic information takes the form of generalized phonetic patterns. A Bag-of-Words Variational Autoencoder (VAE) extracts and models the semantic information, while a phonetic pattern VAE handles the phonetic information. Each uses several regularization techniques for its respective latent space and the information from each is fed to a lyrics decoder to generate novel lyric lines that would satisfy both the Bag-of-Words and phonetic constraints. The experiments show that our model can learn to reconstruct phonetic patterns extracted from text and use them with the Bag-of-Words representations to reconstruct the original lyric lines. Together, the learned representations of phonetic patterns and Bag-of-Words constraints can be used to generate new lyrics.
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Keyword:
artificial intelligence; lyrics; machine learning; natural language processing; phonetics; stylized text; text generation
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URL: http://hdl.handle.net/10012/17941
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Disentanglement of Syntactic Components for Text Generation
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Neural-based Knowledge Transfer in Natural Language Processing
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