Understanding natural language sentences with word embedding and multi-modal interaction

Junpei Zhong, Tetsuya Ogata, Angelo Cangelosi, Chenguang Yang

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    3 Citations (Scopus)

    Abstract

    Understanding and grounding human commands with natural languages have been a fundamental requirement for service robotic applications. Although there have been several attempts toward this goal, the bottleneck still exists to store and process the corpora of natural language in an interaction system. Currently, the neural- and statistical-based (N&S) natural language processing have shown potential to solve this problem. With the availability of large data-sets nowadays, these processing methods are able to extract semantic relationships while parsing a corpus of natural language (NL) text without much human design, compared with the rule-based language processing methods. In this paper, we show that how two N&S based word embedding methods, called Word2vec and GloVe, can be used in natural language understanding as pre-training tools in a multi-modal environment. Together with two different multiple time-scale recurrent neural models, they form hybrid neural language understanding models for a robot manipulation experiment.

    Original languageEnglish
    Title of host publication7th Joint IEEE International Conference on Development and Learning and on Epigenetic Robotics, ICDL-EpiRob 2017
    PublisherInstitute of Electrical and Electronics Engineers Inc.
    Pages184-189
    Number of pages6
    Volume2018-January
    ISBN (Electronic)9781538637159
    DOIs
    Publication statusPublished - 2018 Apr 2
    Event7th Joint IEEE International Conference on Development and Learning and on Epigenetic Robotics, ICDL-EpiRob 2017 - Lisbon, Portugal
    Duration: 2017 Sep 182017 Sep 21

    Other

    Other7th Joint IEEE International Conference on Development and Learning and on Epigenetic Robotics, ICDL-EpiRob 2017
    CountryPortugal
    CityLisbon
    Period17/9/1817/9/21

    ASJC Scopus subject areas

    • Artificial Intelligence
    • Mechanical Engineering
    • Control and Optimization
    • Developmental Neuroscience

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