Correlating Neural and Symbolic Representations of Language

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    Abstract

    Analysis methods which enable us to better understand the representations and functioning of neural models of language are increasingly needed as deep learning becomes the dominant approach in NLP. Here we present two methods based on Representational Similarity Analysis (RSA) and Tree Kernels (TK) which allow us to directly quantify how strongly the information encoded in neural activation patterns corresponds to information represented by symbolic structures such as syntax trees. We first validate our methods on the case of a simple synthetic language for arithmetic expressions with clearly defined syntax and semantics, and show that they exhibit the expected pattern of results. We then our methods to correlate neural representations of English sentences with their constituency parse trees.
    Original languageEnglish
    Title of host publicationProceedings of the 57th Annual Meeting of the Association for Computational Linguistics
    Place of PublicationFlorence, Italy
    PublisherAssociation for Computational Linguistics
    Pages2952-2962
    Number of pages11
    Publication statusPublished - 1 Jul 2019

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