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RETRIEVING SPEAKER INFORMATION FROM PERSONALIZED ACOUSTIC MODELS FOR SPEECH RECOGNITION
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In: IEEE ICASSP 2022 ; https://hal.archives-ouvertes.fr/hal-03539741 ; IEEE ICASSP 2022, 2022, Singapour, Singapore (2022)
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From FreEM to D'AlemBERT ; From FreEM to D'AlemBERT: a Large Corpus and a Language Model for Early Modern French
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In: Proceedings of the 13th Language Resources and Evaluation Conference ; https://hal.inria.fr/hal-03596653 ; Proceedings of the 13th Language Resources and Evaluation Conference, European Language Resources Association, Jun 2022, Marseille, France (2022)
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A gentle introduction to Girard's Transcendental Syntax for the linear logician
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In: https://hal.archives-ouvertes.fr/hal-02977750 ; 2022 (2022)
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Learning and controlling the source-filter representation of speech with a variational autoencoder
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In: https://hal.archives-ouvertes.fr/hal-03650569 ; 2022 (2022)
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Hippocampal ensembles represent sequential relationships among an extended sequence of nonspatial events.
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In: Nature communications, vol 13, iss 1 (2022)
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Changes in the midst of a construction network: a diachronic construction grammar approach to complex prepositions denoting internal location
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In: ISSN: 0936-5907 ; EISSN: 1613-3641 ; Cognitive Linguistics ; https://halshs.archives-ouvertes.fr/halshs-03637056 ; Cognitive Linguistics, De Gruyter, 2022, ⟨10.1515/cog-2021-0128⟩ (2022)
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Changes in the midst of a construction network: a diachronic construction grammar approach to complex prepositions denoting internal location
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In: ISSN: 0936-5907 ; EISSN: 1613-3641 ; Cognitive Linguistics ; https://halshs.archives-ouvertes.fr/halshs-03637056 ; Cognitive Linguistics, De Gruyter, In press, ⟨10.1515/cog-2021-0128⟩ (2022)
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Le modèle Transformer: un « couteau suisse » pour le traitement automatique des langues
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In: Techniques de l'Ingenieur ; https://hal.archives-ouvertes.fr/hal-03619077 ; Techniques de l'Ingenieur, Techniques de l'ingénieur, 2022, ⟨10.51257/a-v1-in195⟩ ; https://www.techniques-ingenieur.fr/base-documentaire/innovation-th10/innovations-en-electronique-et-tic-42257210/transformer-des-reseaux-de-neurones-pour-le-traitement-automatique-des-langues-in195/ (2022)
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Imputing Out-of-Vocabulary Embeddings with LOVE Makes Language Models Robust with Little Cost
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In: ACL 2022 - 60th Annual Meeting of the Association for Computational Linguistics ; https://hal.archives-ouvertes.fr/hal-03613101 ; ACL 2022 - 60th Annual Meeting of the Association for Computational Linguistics, May 2022, Dublin, Ireland (2022)
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Imputing out-of-vocabulary embeddings with LOVE makes language models robust with little cost
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In: ACL 2022 - 60th Annual Meeting of the Association for Computational Linguistics ; https://hal.archives-ouvertes.fr/hal-03613101 ; ACL 2022 - 60th Annual Meeting of the Association for Computational Linguistics, May 2022, Dublin, Ireland (2022)
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| 11 |
Structured, flexible, and robust: comparing linguistic plans and explanations generated by humans and large language models ...
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From bag-of-words towards natural language: adapting topic models to avoid stop word removal ...
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Abstract:
Topic models such as latent Dirichlet allocation (LDA) aim to identify latent topics within text corpora. However, although LDA-type models fall into the category of Natural Language Processing, the actual model input is heavily modified from the original natural language. Among other things, this is typically done by removing specific terms, which arguably might also remove information. In this paper, an extension to LDA is proposed called uLDA, which seeks to incorporate some of these formerly eliminated terms -- namely stop words -- to match natural topics more closely. After developing and evaluating the new extension on established fit measures, uLDA is then tasked with approximating human-perceived topics. For this, a ground truth for topic labels is generated using a human-based experiment. These values are then used as a reference to be matched by the model output. Results show that the new extension outperforms traditional topic models regarding out-of-sample fit across all data sets and regarding ... : Topic-Modelle wie die latente Dirichlet-Allokation (LDA) zielen darauf ab, latente Themen in Textkorpora zu identifizieren. Obwohl Modelle vom Typ LDA in die Kategorie der Verarbeitung natürlicher Sprache fallen, wird die eigentliche Modelleingabe jedoch stark von der ursprünglichen natürlichen Sprache abgewandelt. Dies geschieht u. a. durch das Entfernen bestimmter Begriffe, wodurch allerdings auch Informationen verloren gehen können. In dieser Arbeit wird eine Erweiterung von LDA vorgeschlagen, die uLDA genannt wird und versucht, einige dieser zuvor eliminierten Begriffe - sogenannte Stoppwörter - in das Modell mit einzubeziehen, um die natürlichen Themen besser abzubilden. Nach der Entwicklung und Evaluierung der neuen Erweiterung anhand etablierter Anpassungsmaße wird uLDA dann mit der Aufgabe betraut, vom Menschen wahrgenommene Themen zu approximieren. Zu diesem Zweck wird eine Grundwahrheit für die Themenmarkierung durch ein Experiment mit menschlichen Teilnehmern erzeugt. Diese Werte werden dann als ...
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Keyword:
Bayes-Lernen; Unüberwachtes Lernen; Computerlinguistik; Hierarchical bayes model; Topic models LDA
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URL: https://dx.doi.org/10.17904/ku.opus-726
https://opus4.kobv.de/opus4-ku-eichstaett/frontdoor/index/index/docId/726
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| 13 |
A Collection of Classroom Instruction ... : A Collection of Classroom Instruction ...
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Biodiversity: how big is our global biodiversity debt and what can we do about it? ...
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Bayesian data analysis in the phonetic sciences: A tutorial introduction ...
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How Cognitive Abilities May Support Children’s Bilingual Literacy Development in a Multilingual Society ...
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On the Transferability of Pre-trained Language Models for Low-Resource Programming Languages ...
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Chen, Fuxiang. - : Federated Research Data Repository / dépôt fédéré de données de recherche, 2022
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