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Robot Language Acquisition Based on Sequence-to-Sequence Learning

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dc.contributor.author Thu, Ye Kyaw
dc.contributor.author Takabuchi, Kenta
dc.contributor.author Fukai, Kaisei
dc.contributor.author Iwahashi, Naoto
dc.contributor.author Kunishima, Takeo
dc.date.accessioned 2019-07-15T05:59:38Z
dc.date.available 2019-07-15T05:59:38Z
dc.date.issued 2017-02-16
dc.identifier.uri http://onlineresource.ucsy.edu.mm/handle/123456789/897
dc.description This work was supported by JSPS KAKENHI (grant number 15K00244) and JST CREST (“Symbol Emergence in Robotics for Future Human-Machine Collaboration”). en_US
dc.description.abstract Language acquisition for robot is a challenging topic in the artificial intelligence research area and essential for natural communication between robot and human. In this paper, we proposed language acquisition directly from motion video and user’s utterance with multimodal machine learnings without prior knowledge of linguistic or language specific information. Translation between acquired conceptual structure and syllable sequences of a human language (e.g. Japanese language) was carried out by applying machine translation methodologies including sequence-to-sequence learning. Experiments on language acquisition with 500 videos show Encoder- Decoder, Encoder-Decoder with Attention models are able to achieve equal translation performance of baselines that was prepared manually. en_US
dc.language.iso en en_US
dc.publisher Fifteenth International Conference on Computer Applications (ICCA 2017) en_US
dc.title Robot Language Acquisition Based on Sequence-to-Sequence Learning en_US
dc.type Article en_US

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