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Grounding Hindsight Instructions in Multi-Goal Reinforcement Learning for Robotics ...
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LipSound2: Self-Supervised Pre-Training for Lip-to-Speech Reconstruction and Lip Reading ...
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Neural Network Learning for Robust Speech Recognition
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Qu, Leyuan. - : Staats- und Universitätsbibliothek Hamburg Carl von Ossietzky, 2021
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Towards a self-organizing pre-symbolic neural model representing sensorimotor primitives ...
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Conversational Language Learning for Human-Robot Interaction
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Crossmodal Language Grounding in an Embodied Neurocognitive Model
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In: Front Neurorobot (2020)
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Incorporating End-to-End Speech Recognition Models for Sentiment Analysis ...
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Towards Dialogue-based Navigation with Multivariate Adaptation driven by Intention and Politeness for Social Robots ...
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GradAscent at EmoInt-2017: Character- and Word-Level Recurrent Neural Network Models for Tweet Emotion Intensity Detection ...
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Syntactic Reanalysis in Language Models for Speech Recognition
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In: 2017 Joint IEEE International Conference on Development and Learning and Epigenetic Robotics (ICDL-EpiRob) ; https://hal.inria.fr/hal-01558462 ; 2017 Joint IEEE International Conference on Development and Learning and Epigenetic Robotics (ICDL-EpiRob), Sep 2017, Lisbon, Portugal ; http://icdl-epirob.org/ (2017)
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Interactive Natural Language Acquisition in a Multi-modal Recurrent Neural Architecture ...
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Recurrent Neural Network for Syntax Learning with Flexible Predicates for Robotic Architectures
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In: The Sixth Joint IEEE International Conference Developmental Learning and Epigenetic Robotics (ICDL-EPIROB) ; https://hal.inria.fr/hal-01417697 ; The Sixth Joint IEEE International Conference Developmental Learning and Epigenetic Robotics (ICDL-EPIROB), Sep 2016, Cergy, France ; http://icdl-epirob.org/ (2016)
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Semantic Role Labelling for Robot Instructions using Echo State Networks
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In: European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN) ; https://hal.inria.fr/hal-01417701 ; European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN), Apr 2016, Bruges, Belgium ; https://www.elen.ucl.ac.be/esann/index.php?pg=esann16_programme (2016)
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Using Natural Language Feedback in a Neuro-inspired Integrated Multimodal Robotic Architecture
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In: 25th IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN) ; https://hal.inria.fr/hal-01417706 ; 25th IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN), Aug 2016, New York City, United States. pp.52 - 57, ⟨10.1109/ROMAN.2016.7745090⟩ ; http://www.tc.columbia.edu/conferences/roman2016/ (2016)
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| 15 |
Natural language acquisition in recurrent neural architectures ; Erwerb von natürlicher Sprache in rekurrenten neuronalen Architekturen
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Heinrich, Stefan. - : Staats- und Universitätsbibliothek Hamburg Carl von Ossietzky, 2016
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A Recurrent Neural Network for Multiple Language Acquisition: Starting with English and French
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In: Proceedings of the NIPS Workshop on Cognitive Computation: Integrating Neural and Symbolic Approaches (CoCo 2015) ; https://hal.inria.fr/hal-02561258 ; Proceedings of the NIPS Workshop on Cognitive Computation: Integrating Neural and Symbolic Approaches (CoCo 2015), Dec 2015, Montreal, Canada ; http://ceur-ws.org/Vol-1583/ (2015)
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Abstract:
International audience ; How humans acquire language, and in particular two or more different languages with the same neural computing substrate, is still an open issue. To address this issue we suggest to build models that are able to process any language from the very beginning. Here we propose a developmental and neuro-inspired approach that processes sentences word by word with no prior knowledge of the semantics of the words. Our model has no "pre-wired" structure but only random and learned connections: it is based on Reservoir Computing. Our previous model has been implemented in the context of robotic platforms where users could teach basics of the English language to instruct a robot to perform actions. In this paper, we add the ability to process infrequent words, so we could keep our vocabulary size very small while processing natural language sentences. Moreover, we extend this approach to the French language and demonstrate that the network can learn both languages at the same time. Even with small corpora the model is able to learn and generalize in monolingual and bilingual conditions. This approach promises to be a more practical alternative for small corpora of different languages than other supervised learning methods relying on big data sets or more hand-crafted parsers requiring more manual encoding effort.
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Keyword:
[INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG]; [INFO.INFO-NE]Computer Science [cs]/Neural and Evolutionary Computing [cs.NE]; [INFO.INFO-RB]Computer Science [cs]/Robotics [cs.RO]; [SCCO.LING]Cognitive science/Linguistics; [SDV.NEU]Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC]
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URL: https://hal.inria.fr/hal-02561258
https://hal.inria.fr/hal-02561258/file/CoCoNIPS_2015_paper_14.pdf
https://hal.inria.fr/hal-02561258/document
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Toward a self-organizing pre-symbolic neural model representing sensorimotor primitives
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| 18 |
Toward a self-organizing pre-symbolic neural model representing sensorimotor primitives
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