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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 Page: 1 2 3 4 5...19 Hits 1 – 20 of 379 1 Retrieval-Based Transformer Pseudocode Generation Anas Alokla; Walaa Gad; Waleed Nazih; Mustafa Aref; Abdel-Badeeh Salem In: Mathematics; Volume 10; Issue 4; Pages: 604 (2022) BASE Show details 2 Representation learning of natural language and its application to language understanding and generation Gong, Hongyu. - 2022 BASE Show details 3 Controlled Generation of Stylized Text Using Semantic and Phonetic Representations Gudmundsson, Egill Ian. - : University of Waterloo, 2022 BASE Show details 4 Disentanglement of Syntactic Components for Text Generation Das, Utsav Tushar. - : University of Waterloo, 2022 BASE Show details 5 Desarrollo de un generador automático de ejercicios gramaticales de euskera Scarinci Zabaleta, Dana. - : Universitat Oberta de Catalunya (UOC), 2022 BASE Show details 6 Natural Language Generation : From Data Creation to Evaluation via Modelling ; Génération en langue naturelle : de la création des données à l'évaluation, en passant par la modélisation Shimorina, Anastasia. - : HAL CCSD, 2021 In: https://hal.univ-lorraine.fr/tel-03254708 ; Computation and Language [cs.CL]. Université de Lorraine, 2021. English. ⟨NNT : 2021LORR0080⟩ (2021) Abstract: Natural language generation is a process of generating a natural language text from some input. This input can be texts, documents, images, tables, knowledge graphs, databases, dialogue acts, meaning representations, etc. Recent methods in natural language generation, mostly based on neural modelling, have yielded significant improvements in the field. Despite this recent success, numerous issues with generation prevail, such as faithfulness to the source, developing multilingual models, few-shot generation. This thesis explores several facets of natural language generation from creating training datasets and developing models to evaluating proposed methods and model outputs. In this thesis, we address the issue of multilinguality and propose possible strategies to semi-automatically translate corpora for data-to-text generation. We show that named entities constitute a major stumbling block in translation exemplified by the English-Russian translation pair. We proceed to handle rare entities in data-to-text modelling exploring two mechanisms: copying and delexicalisation. We demonstrate that rare entities strongly impact performance and that the impact of these two mechanisms greatly varies depending on how datasets are constructed. Getting back to multilinguality, we also develop a modular approach for shallow surface realisation in several languages. Our approach splits the surface realisation task into three submodules: word ordering, morphological inflection and contraction generation. We show, via delexicalisation, that the word ordering component mainly depends on syntactic information. Along with the modelling, we also propose a framework for error analysis, focused on word order, for the shallow surface realisation task. The framework enables to provide linguistic insights into model performance on the sentence level and identify patterns where models underperform. Finally, we also touch upon the subject of evaluation design while assessing automatic and human metrics, highlighting the difference between the sentence-level and system-level type of evaluation.Natural language generation is a process of generating a natural language text from some input. This input can be texts, documents, images, tables, knowledge graphs, databases, dialogue acts, meaning representations, etc. Recent methods in natural language generation, mostly based on neural modelling, have yielded significant improvements in the field. Despite this recent success, numerous issues with generation prevail, such as faithfulness to the source, developing multilingual models, few-shot generation. This thesis explores several facets of natural language generation from creating training datasets and developing models to evaluating proposed methods and model outputs. In this thesis, we address the issue of multilinguality and propose possible strategies to semi-automatically translate corpora for data-to-text generation. We show that named entities constitute a major stumbling block in translation exemplified by the English-Russian translation pair. We proceed to handle rare entities in data-to-text modelling exploring two mechanisms: copying and delexicalisation. We demonstrate that rare entities strongly impact performance and that the impact of these two mechanisms greatly varies depending on how datasets are constructed. Getting back to multilinguality, we also develop a modular approach for shallow surface realisation in several languages. Our approach splits the surface realisation task into three submodules: word ordering, morphological inflection and contraction generation. We show, via delexicalisation, that the word ordering component mainly depends on syntactic information. Along with the modelling, we also propose a framework for error analysis, focused on word order, for the shallow surface realisation task. The framework enables to provide linguistic insights into model performance on the sentence level and identify patterns where models underperform. Finally, we also touch upon the subject of evaluation design while assessing automatic and human metrics, highlighting the difference between the sentence-level and system-level type of evaluation. ; La génération en langue naturelle (natural language generation, NLG) est le processus qui consiste à générer du texte dans une langue naturelle à partir de données d’entrée. Ces entrées peuvent prendre la forme de textes, de documents, d’images, de tableaux, de graphes (réseaux de connaissances), de bases de données, d’actes de dialogue, ou d’autres représentations sémantiques. Les méthodes récentes en NLG, principalement basées sur des modèles neuronaux, ont apporté des améliorations significatives. Malgré ces récents progrès, de nombreux problèmes liés à la tâche de génération subsistent, tels que celui de la fidélité aux données d’entrée, du développement de modèles multilingues, ou de la génération à partir de peu d’exemples. Cette thèse explore trois aspects de la NLG : tout d’abord, la création de données d’apprentissage, puis le développement de modèles de génération, et enfin l’évaluation des méthodes proposées. Nous abordons la question du multilinguisme et proposons des stratégies de traduction semi-automatique de corpus destinés à l’entraînement de modèles de NLG. Nous montrons que les entités nommées constituent un obstacle majeur dans la réalisation de la tâche de traduction, ici considérée de l’anglais vers le russe. Nous décrivons ensuite deux méthodes de traitement des entités rares dans les données d’apprentissages des modèles de NLG : la copie et la délexicalisation. Nous démontrons que l’effet de ces deux mécanismes varie fortement selon la manière dont les données sont construites, et que les entités rares ont un impact important sur les performances des modèles. Concernant la génération multilingue, nous développons une approche modulaire de réalisation de surface superficielle (shallow surface realisation, SSR) pour plusieurs langues. Notre approche consiste à diviser la tâche de SSR en trois composantes : l’ordonnancement des mots, l’inflexion morphologique et la génération de contractions. Nous montrons, via la délexicalisation, que la composante d’ordonnancement s’appuie principalement sur les informations syntaxiques. En plus de nos contributions concernant la modélisation, nous proposons un cadre d’analyse des erreurs axé sur l’ordre des mots, pour la tâche de SSR. Ce cadre permet d’obtenir un aperçu linguistique des performances des modèles au niveau de la phrase et d’identifier les cas où un modèle échoue. Enfin, nous abordons le sujet de l’évaluation de manière plus générale et comparons différentes métriques automatiques et humaines ; nous soulignons la différence entre les méthodes d’évaluation au niveau de la phrase et les méthodes d’évaluations au niveau du corpus. Keyword: [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI]; [INFO.INFO-CL]Computer Science [cs]/Computation and Language [cs.CL]; Analyse d’erreurs; Data-to-text generation; Error analysis; Évaluation; Evaluation; Génération à partir de données; Génération en langue naturelle; Natural language generation; Réalisation de surface; Surface realisation URL: https://hal.univ-lorraine.fr/tel-03254708/document https://hal.univ-lorraine.fr/tel-03254708 https://hal.univ-lorraine.fr/tel-03254708/file/DDOC_T_2021_0080_SHIMORINA.pdf BASE Hide details 7 THEaiTRobot 1.0 Rosa, Rudolf; Dušek, Ondřej; Kocmi, Tom. - : Charles University, Faculty of Mathematics and Physics, Institute of Formal and Applied Linguistics (UFAL), 2021. : The Švanda Theatre in Smíchov, 2021. : The Academy of Performing Arts in Prague, Theatre Faculty (DAMU), 2021 BASE Show details 8 Zur Darstellung eines mehrstufigen Prototypbegriffs in der multilingualen automatischen Sprachgenerierung: vom Korpus über word embeddings bis hin zum automatischen Wörterbuch Domínguez Vázquez, María José In: Lexikos; Vol. 31 (2021); 20-50 ; 2224-0039 (2021) BASE Show details 9 Recent Advances in Intelligent Source Code Generation: A Survey on Natural Language Based Studies Chen Yang; Yan Liu; Changqing Yin In: Entropy ; Volume 23 ; Issue 9 (2021) BASE Show details 10 Lyrics and vocal melody generation conditioned on accompaniment ... : Αυτόματη παραγωγή στίχων και φωνητικής μελωδίας βάσει της μουσικής υπόκρουσης με τεχνικές βαθιάς μηχανικής μάθησης ... Melistas, Thomas. - : National Technological University of Athens, 2021 BASE Show details 11 TSDAE: Using Transformer-based Sequential Denoising Auto-Encoder for Unsupervised Sentence Embedding Learning ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; ., Iryna; Reimers, Nils. - : Underline Science Inc., 2021 BASE Show details 12 Truth-Conditional Captions for Time Series Data ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; Berg-Kirkpatrick, Taylor; Jhamtani, Harsh. - : Underline Science Inc., 2021 BASE Show details 13 Automatic Text Evaluation through the Lens of Wasserstein Barycenters ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; Clavel, Chloe; Colombo, Pierre. - : Underline Science Inc., 2021 BASE Show details 14 Looking for Confirmations: An Effective and Human-Like Visual Dialogue Strategy ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; Bernardi, Raffaella; Testoni, Alberto. - : Underline Science Inc., 2021 BASE Show details 15 CrossVQA: Scalably Generating Benchmarks for Systematically Testing VQA Generalization ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; ., Piyush; Akula, Arjun. - : Underline Science Inc., 2021 BASE Show details 16 IndoNLG: Benchmark and Resources for Evaluating Indonesian Natural Language Generation ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; ., Sebastian; Bahar, Syafri. - : Underline Science Inc., 2021 BASE Show details 17 Building the Directed Semantic Graph for Coherent Long Text Generation ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; Wang, Ziao. - : Underline Science Inc., 2021 BASE Show details 18 Graphine: A Dataset for Graph-aware Terminology Definition Generation ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; Gu, Yiyang; Liu, Zequn. - : Underline Science Inc., 2021 BASE Show details 19 Error-Sensitive Evaluation for Ordinal Target Variables ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; Chen, David; Courtland, Maury. - : Underline Science Inc., 2021 BASE Show details 20 Data-to-text Generation by Splicing Together Nearest Neighbors ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; Backurs, Arturs; Stratos, Karl. - : Underline Science Inc., 2021 BASE Show details Page: 1 2 3 4 5...19

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