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Cross-Situational Learning Towards Robot Grounding
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In: https://hal.archives-ouvertes.fr/hal-03628290 ; 2022 (2022)
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Cross-Situational Learning Towards Robot Grounding
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In: https://hal.archives-ouvertes.fr/hal-03628290 ; 2022 (2022)
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Abstract:
How do children acquire language through unsupervised or noisy supervision? How do their brain process language? We take this perspective to machine learning and robotics, where part of the problem is understanding how language models can perform grounded language acquisition through noisy supervision and discussing how they can account for brain learning dynamics. Most prior works have tracked the co-occurrence between single words and referents to model how infants learn wordreferent mappings. This paper studies cross-situational learning (CSL) with full sentences: we want to understand brain mechanisms that enable children to learn mappings between words and their meanings from full sentences in early language learning. We investigate the CSL task on a few training examples with two sequence-based models: (i) Echo State Networks (ESN) and (ii) Long-Short Term Memory Networks (LSTM). Most importantly, we explore several word representations including One-Hot, GloVe, pretrained BERT, and fine-tuned BERT representations (last layer token representations) to perform the CSL task. We apply our approach to three diverse datasets (two grounded language datasets and a robotic dataset) and observe that (1) One-Hot, GloVe, and pretrained BERT representations are less efficient when compared to representations obtained from fine-tuned BERT. (2) ESN online with final learning (FL) yields superior performance over ESN online continual learning (CL), offline learning, and LSTMs, indicating the more biological plausibility of ESNs and the cognitive process of sentence reading. (2) LSTM with fewer hidden units showcases higher performance for small datasets, but LSTM with more hidden units is Cross-Situational Learning needed to perform reasonably well on larger corpora. (4) ESNs demonstrate better generalization than LSTM models for increasingly large vocabularies. Overall, these models are able to learn from scratch to link complex relations between words and their corresponding meaning concepts, handling polysemous and synonymous words. Moreover, we argue that such models can extend to help current human-robot interaction studies on language grounding and better understand children's developmental language acquisition. We make the code publicly available * .
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Keyword:
[INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI]; [INFO.INFO-CL]Computer Science [cs]/Computation and Language [cs.CL]; [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]; [SDV.NEU]Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC]; BERT; cross-situational learning; echo state networks; grounded language; LSTM
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URL: https://hal.archives-ouvertes.fr/hal-03628290/document https://hal.archives-ouvertes.fr/hal-03628290/file/Journal_of_Social_and_Robotics.pdf https://hal.archives-ouvertes.fr/hal-03628290
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What does the Canary Say? Low-Dimensional GAN Applied to Birdsong
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In: https://hal.inria.fr/hal-03244723 ; 2021 (2021)
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What does the Canary Say? Low-Dimensional GAN Applied to Birdsong
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In: https://hal.inria.fr/hal-03244723 ; 2021 (2021)
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Which Hype for my New Task? Hints and Random Search for Reservoir Computing Hyperparameters
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In: ICANN 2021 - 30th International Conference on Artificial Neural Networks ; https://hal.inria.fr/hal-03203318 ; ICANN 2021 - 30th International Conference on Artificial Neural Networks, Sep 2021, Bratislava, Slovakia (2021)
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Canary Song Decoder: Transduction and Implicit Segmentation with ESNs and LTSMs
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In: https://hal.inria.fr/hal-03203374 ; 2021 (2021)
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Which Hype for my New Task? Hints and Random Search for Reservoir Computing Hyperparameters
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In: https://hal.inria.fr/hal-03203318 ; 2021 (2021)
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Canary Song Decoder: Transduction and Implicit Segmentation with ESNs and LTSMs
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In: ICANN 2021 - 30th International Conference on Artificial Neural Networks ; https://hal.inria.fr/hal-03203374 ; ICANN 2021 - 30th International Conference on Artificial Neural Networks, Sep 2021, Bratislava, Slovakia. pp.71--82, ⟨10.1007/978-3-030-86383-8_6⟩ ; https://link.springer.com/chapter/10.1007/978-3-030-86383-8_6 (2021)
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Hierarchical-Task Reservoir for Online Semantic Analysis from Continuous Speech
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In: ISSN: 2162-237X ; IEEE Transactions on Neural Networks and Learning Systems ; https://hal.inria.fr/hal-03031413 ; IEEE Transactions on Neural Networks and Learning Systems, IEEE, 2021, ⟨10.1109/TNNLS.2021.3095140⟩ ; https://ieeexplore.ieee.org/abstract/document/9548713/metrics#metrics (2021)
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Editorial: Language and Robotics
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In: ISSN: 2296-9144 ; Frontiers in Robotics and AI ; https://hal.inria.fr/hal-03533733 ; Frontiers in Robotics and AI, Frontiers Media S.A., 2021, 8, ⟨10.3389/frobt.2021.674832⟩ (2021)
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Learning to Parse Sentences with Cross-Situational Learning using Different Word Embeddings Towards Robot Grounding ...
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Cross-Situational Learning with Reservoir Computing for Language Acquisition Modelling
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In: 2020 International Joint Conference on Neural Networks (IJCNN 2020) ; https://hal.inria.fr/hal-02594725 ; 2020 International Joint Conference on Neural Networks (IJCNN 2020), Jul 2020, Glasgow, Scotland, United Kingdom ; https://wcci2020.org/ (2020)
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Hierarchical-Task Reservoir for Anytime POS Tagging from Continuous Speech
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In: 2020 International Joint Conference on Neural Networks (IJCNN 2020) ; https://hal.inria.fr/hal-02594495 ; 2020 International Joint Conference on Neural Networks (IJCNN 2020), Jul 2020, Glasgow, Scotland, United Kingdom ; https://wcci2020.org/ (2020)
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Language Acquisition with Echo State Networks: Towards Unsupervised Learning
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In: ICDL 2020 - IEEE International Conference on Development and Learning ; https://hal.inria.fr/hal-02926613 ; ICDL 2020 - IEEE International Conference on Development and Learning, Oct 2020, Valparaiso / Virtual, Chile (2020)
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A Journey in ESN and LSTM Visualisations on a Language Task
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In: https://hal.inria.fr/hal-03030248 ; 2020 (2020)
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Recurrent Neural Networks Models for Developmental Language Acquisition: Reservoirs Outperform LSTMs
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In: SNL 2020 - 12th Annual Meeting of the Society for the Neurobiology of Language ; https://hal.inria.fr/hal-03146558 ; SNL 2020 - 12th Annual Meeting of the Society for the Neurobiology of Language, Oct 2020, Virtual Edition, Canada (2020)
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Learning to Parse Grounded Language using Reservoir Computing
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In: ICDL-Epirob 2019 - Joint IEEE 9th International Conference on Development and Learning and Epigenetic Robotics ; https://hal.inria.fr/hal-02422157 ; ICDL-Epirob 2019 - Joint IEEE 9th International Conference on Development and Learning and Epigenetic Robotics, Aug 2019, Olso, Norway. ⟨10.1109/devlrn.2019.8850718⟩ ; https://ieeexplore.ieee.org/abstract/document/8850718 (2019)
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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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A Reservoir Model for Intra-Sentential Code-Switching Comprehension in French and English
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In: CogSci'19 - 41st Annual Meeting of the Cognitive Science Society ; https://hal.inria.fr/hal-02432831 ; CogSci'19 - 41st Annual Meeting of the Cognitive Science Society, Jul 2019, Montréal, Canada ; https://cognitivesciencesociety.org/cogsci-2019/ (2019)
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Replication of Laje & Mindlin's model producing synthetic syllables
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In: European Birdsong Meeting ; https://hal.inria.fr/hal-01964522 ; European Birdsong Meeting, Apr 2018, Odense, Denmark. 2018 (2018)
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