Presentation | 2020-01-26 Modeling for Infant Vocabulary Acquisition System using Deep Reinforcement Learning Masaki Taguchi, Yasuhiro Minami, |
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PDF Download Page | PDF download Page Link |
Abstract(in Japanese) | (See Japanese page) |
Abstract(in English) | We propose an infant vocabulary acquisition model that identifies psychological infant vocabulary development findings (joint attention, learning bias, and understanding intention) associated with symbol grounding using deep reinforcement learning. In deep reinforcement learning, we use DDQN and LSTM, which treats the time sequence data of long-term dependency. We use the features obtained from real objects to ground words to those objects. Simulation experiments investigated the symbol-grounding abilities of the model and the appearances of psychological findings in the process of infant word acquisition. We confirmed that our proposed model can ground words to the objects and achieved joint attention and understanding intention. We also confirmed that it acquires (by learning) noun bias, which is thought to innately exist by many psychologists. These results confirm that the multiple psychological phenomena of language acquisition can be modeled using the latest neural network. |
Keyword(in Japanese) | (See Japanese page) |
Keyword(in English) | double deep q-network / long short-term memory / image recognition / feature extraction |
Paper # | HCS2019-73 |
Date of Issue | 2020-01-18 (HCS) |
Conference Information | |
Committee | HCS |
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Conference Date | 2020/1/25(2days) |
Place (in Japanese) | (See Japanese page) |
Place (in English) | Room407, J:COM HorutoHall OITA (Oita) |
Topics (in Japanese) | (See Japanese page) |
Topics (in English) | Psychology and Life-stage of Communication, etc. |
Chair | Masafumi Matsuda(NTT) |
Vice Chair | Tomoo Inoue(Univ. of Tsukuba) / Yugo Hayashi(Ritsumeikan Univ.) |
Secretary | Tomoo Inoue(Kanazawa Inst. of Tech.) / Yugo Hayashi(Osaka Electro-Comm. Univ.) |
Assistant | Tomoko Kanda(Osaka Inst. of Tech.) / Kazuki Takashima(Tohoku Univ.) / Ken Fujiwara(Osaka Univ. of Economic) / Kazunori Terada(Gifu Univ.) / Atsushi Kimura(Nihon Univ.) / HUANG HUNGHSUAN(Riken) |
Paper Information | |
Registration To | Technical Committee on Human Communication Science |
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Language | JPN |
Title (in Japanese) | (See Japanese page) |
Sub Title (in Japanese) | (See Japanese page) |
Title (in English) | Modeling for Infant Vocabulary Acquisition System using Deep Reinforcement Learning |
Sub Title (in English) | |
Keyword(1) | double deep q-network |
Keyword(2) | long short-term memory |
Keyword(3) | image recognition |
Keyword(4) | feature extraction |
1st Author's Name | Masaki Taguchi |
1st Author's Affiliation | The University of Electro-Communications(UEC) |
2nd Author's Name | Yasuhiro Minami |
2nd Author's Affiliation | The University of Electro-Communications(UEC) |
Date | 2020-01-26 |
Paper # | HCS2019-73 |
Volume (vol) | vol.119 |
Number (no) | HCS-394 |
Page | pp.pp.111-116(HCS), |
#Pages | 6 |
Date of Issue | 2020-01-18 (HCS) |