Introduction to the special issue on learning semantics

Antoine Bordes, Léon Bottou, Ronan Collobert, Dan Roth, Jason Weston, Luke Zettlemoyer

    Research output: Contribution to journalReview articlepeer-review

    Abstract

    The 2014 Special Issue of Machine Learning discusses several papers on learning semantics. The first paper of the special issue, 'From Machine Learning to Machine Reasoning' by Léon Bottou is an essay which attempts to bridge trainable systems, like neural networks, and sophisticated 'all-purpose' inference mechanisms, such as logical or probabilistic inference. The paper 'Learning Perceptually Grounded Word Meanings from Unaligned Parallel Data' by Stefanie Tellex, Pratiksha Thaker, Joshua Joseph and Nicholas Roy describes an approach to map natural language commands to actions for a forklift control task. The paper 'Interactive Relational Reinforcement Learning of Concept Semantics' by Matthias Nickles and Achim Rettinger presents a Relational Reinforcement Learning (RRL) approach for learning denotational concept semantics using symbolic interaction of artificial agents with human users.

    Original languageEnglish (US)
    Pages (from-to)127-131
    Number of pages5
    JournalMachine Learning
    Volume94
    Issue number2
    DOIs
    StatePublished - Feb 2014

    ASJC Scopus subject areas

    • Software
    • Artificial Intelligence

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