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Teach Your Robot Your Language! Trainable Neural Parser for Modelling Human Sentence Processing: Examples for 15 Languages
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In: ISSN: 2379-8920 ; EISSN: 2379-8939 ; IEEE Transactions on Cognitive and Developmental Systems ; https://hal.inria.fr/hal-01964541 ; IEEE Transactions on Cognitive and Developmental Systems, Institute of Electrical and Electronics Engineers, Inc, 2019, ⟨10.1109/TCDS.2019.2957006⟩ ; https://doi.org/10.1109/tcds.2019.2957006 (2019)
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Abstract:
International audience ; We present a Recurrent Neural Network (RNN) that performs thematic role assignment and can be used for Human-Robot Interaction (HRI). The RNN is trained to map sentence structures to meanings (e.g. predicates). Previously, we have shown that the model is able to generalize on English and French corpora. In this study, we investigate its ability to adapt to various languages originating from Asia or Europe. We show that it can successfully learn to parse sentences related to home scenarios in fifteen languages: English, German, French, Spanish, Catalan, Basque, Portuguese, Italian, Bulgarian, Turkish, Persian, Hindi, Marathi, Malay and Mandarin Chinese. Moreover, in the corpora we have deliberately included variable complex sentences in order to explore the flexibility of the predicate-like output representations. This demonstrates that (1) the learning principle of our model is not limited to a particular language (or particular sentence structures), but more generic in nature, and (2) it can deal with various kind of representations (not only predicates), which enables users to adapt it to their own needs. As the model is inspired from neuroscience and language acquisition theories, this generic and language independent aspect makes it a good candidate for modelling human sentence processing. Finally, we discuss the potential implementation of the model in a grounded robotic architecture.
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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://doi.org/10.1109/TCDS.2019.2957006
https://hal.inria.fr/hal-01964541
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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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| 3 |
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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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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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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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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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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