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Which Input Abstraction is Better for a Robot Syntax Acquisition Model? Phonemes, Words or Grammatical Constructions?
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In: 2018 Joint IEEE International Conference on Development and Learning and Epigenetic Robotics (ICDL-EpiRob) ; https://hal.inria.fr/hal-01889919 ; 2018 Joint IEEE International Conference on Development and Learning and Epigenetic Robotics (ICDL-EpiRob), Sep 2018, Tokyo, Japan (2018)
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| 22 |
From Phonemes to Sentence Comprehension: A Neurocomputational Model of Sentence Processing for Robots
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In: SBDM2018 Satellite-Workshop on interfaces between Robotics, Artificial Intelligence and Neuroscience ; https://hal.inria.fr/hal-01964524 ; SBDM2018 Satellite-Workshop on interfaces between Robotics, Artificial Intelligence and Neuroscience, May 2018, Paris, France (2018)
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| 23 |
Neural coding of variable song structure in the songbird
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In: EBM 2017 - European Birdsong Meeting ; https://hal.inria.fr/hal-01665824 ; EBM 2017 - European Birdsong Meeting, May 2017, Bordeaux, France. pp.1 ; https://birdsong2017.sciencesconf.org/ (2017)
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| 24 |
From Phonemes to Robot Commands with a Neural Parser
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In: IEEE ICDL-EPIROB Workshop on Language Learning ; https://hal.inria.fr/hal-01665823 ; IEEE ICDL-EPIROB Workshop on Language Learning, Sep 2017, Lisbon, Portugal. pp.1-2 (2017)
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| 25 |
Modelling sentence processing with random recurrent neural networks and applications to robotics
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In: Workshop "The role of the basal ganglia in the interaction between language and other cognitive functions" ; https://hal.inria.fr/hal-01673440 ; Workshop "The role of the basal ganglia in the interaction between language and other cognitive functions", Anne-Catherine Bachoud-Lévi, Maria Giavazzi, Charlotte Jacquemot, Laboratoire de NeuroPsychologie Interventionnelle., Oct 2017, Paris, France ; http://www.ens.fr/agenda/role-basal-ganglia-interaction-between-language-and-other-cognitive-functions/2017-10 (2017)
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Abstract:
National audience ; Primates can learn complex sequences that can be represented in the form of abstract categories, and even more abstract hierarchical structures such as language. In order to study how these abstractions are formed and because of the highly recurrent connectivity in prefrontal cortex (PFC) we model part of it using recurrent neural networks. Particularly, we use the Reservoir Computing paradigm to model PFC and part of the basal ganglia: a recurrent neural network with random connections kept constant models the prefrontal cortex, and a read-out layer (i.e. output layer) models the striatum.This model was trained to perform language syntactic processing; in particular, thematic role assignment: for a given sentence this corresponds to answer the question "Who did what to whom?". Inspiring from language acquisition theories (Tomasello 2003), the model processes categories (i.e. abstractions) of sentences which are called "grammatical constructions" (Goldberg 1995). After training, it is able to (1) process correctly the majority of the grammatical constructions that were not learned, demonstrating generalization capabilities, and (2) to make online predictions (of thematic roles) while processing a grammatical construction. Moreover, we observed that when the model processes less frequent constructions an important shift in output predictions occurs. It is proposed that a significant modification of predictions in a short period of time is responsible for generating Evoked-Related Potentials (ERP) such as the P600 which typically occurs when unusual sentences structures are processed (Hinaut & Dominey 2013).Subsequently, to show the ability of the model to deal with a real-world application, the model was successfully applied in the framework of human-robot interaction for both sentence comprehension and production (Hinaut et al, 2014). Recently, we showed that the very same instance of reservoir could learn both English and French sentences at the same time, suggesting that a common "output" (striatal) representations could be used even in the case of different languages (Hinaut et al, 2015). Moreover, the model is able to learn small corpora in fifteen European or Asian languages with different word order (Hinaut et al, in revision). In a nutshell, this suggests that a random neural network with no prewired structure seems enough to learn the syntax of languages different in structure and in word order.
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Keyword:
[INFO.INFO-NE]Computer Science [cs]/Neural and Evolutionary Computing [cs.NE]; [INFO.INFO-RB]Computer Science [cs]/Robotics [cs.RO]; [SCCO.COMP]Cognitive science/Computer science; [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-01673440
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| 26 |
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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| 27 |
Teach Your Robot Your Language! Trainable Neural Parser for Modelling Human Sentence Processing: Examples for 15 Languages
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In: https://hal.inria.fr/hal-01665807 ; 2017 (2017)
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| 28 |
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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| 29 |
Recurrent Neural Network for Syntax Learning with Flexible Representations
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In: IEEE ICDL-EPIROB Workshop on Language Learning ; https://hal.inria.fr/hal-01417060 ; IEEE ICDL-EPIROB Workshop on Language Learning, Dec 2016, Cergy, France ; https://sites.google.com/site/epirob2016language/ (2016)
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| 30 |
Reservoir Computing for Robot Language Acquisition
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In: IROS Workshop on Machine Learning Methods for High-Level Cognitive Capabilities in Robotics ; https://hal.inria.fr/hal-01417683 ; IROS Workshop on Machine Learning Methods for High-Level Cognitive Capabilities in Robotics, Oct 2016, Daejon, South Korea ; http://mlhlcr2016.tanichu.com/home (2016)
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| 31 |
Recurrent Neural Network Sentence Parser for Multiple Languages with Flexible Meaning Representations for Home Scenarios
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In: IROS Workshop on Bio-inspired Social Robot Learning in Home Scenarios ; https://hal.inria.fr/hal-01417667 ; IROS Workshop on Bio-inspired Social Robot Learning in Home Scenarios, Oct 2016, Daejon, South Korea ; https://www.informatik.uni-hamburg.de/wtm/SocialRobotsWorkshop2016/index.php (2016)
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| 32 |
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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| 33 |
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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| 34 |
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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| 35 |
Exploring the acquisition and production of grammatical constructions through human-robot interaction with echo state networks
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In: ISSN: 1662-5218 ; EISSN: 1662-5218 ; Frontiers in Neurorobotics ; https://hal.inria.fr/hal-02383530 ; Frontiers in Neurorobotics, Frontiers, 2014, 8, ⟨10.3389/fnbot.2014.00016⟩ (2014)
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| 36 |
Exploring the acquisition and production of grammatical constructions through human-robot interaction with echo state networks
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| 37 |
On-Line Learning of Lexical Items and Grammatical Constructions via Speech, Gaze and Action-Based Human-Robot Interaction
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In: INTERSPEECH 2013 - 14th Annual Conference of the International Speech Communication Association ; https://hal.inria.fr/hal-02561340 ; INTERSPEECH 2013 - 14th Annual Conference of the International Speech Communication Association, Aug 2013, Lyon, France ; https://www.isca-speech.org/archive/interspeech_2013/i13_2657.html (2013)
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| 38 |
Real-Time Parallel Processing of Grammatical Structure in the Fronto-Striatal System: A Recurrent Network Simulation Study Using Reservoir Computing
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In: ISSN: 1932-6203 ; EISSN: 1932-6203 ; PLoS ONE ; https://hal.inria.fr/hal-01968923 ; PLoS ONE, Public Library of Science, 2013, 8 (2), pp.e52946. ⟨10.1371/journal.pone.0052946⟩ (2013)
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| 39 |
Real-Time Parallel Processing of Grammatical Structure in the Fronto-Striatal System: A Recurrent Network Simulation Study Using Reservoir Computing
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Online Language Learning to Perform and Describe Actions for Human-Robot Interaction
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In: Post-Graduate Conference on Robotics and Development of Cognition ; https://hal.inria.fr/hal-02561346 ; Post-Graduate Conference on Robotics and Development of Cognition, Sep 2012, Lausanne, Switzerland (2012)
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