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    Probabilistic verb selection for data-to-text generation

    Zhang, Dell and Yuan, J. and Wang, X. and Foster, Adam (2018) Probabilistic verb selection for data-to-text generation. Transactions of the Association for Computational Linguistics 6 , pp. 511-527. ISSN 2307-387X.

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    Abstract

    In data-to-text Natural Language Generation (NLG) systems, computers need to find the right words to describe phenomena seen in the data. This paper focuses on the problem of choosing appropriate verbs to express the direction and magnitude of a percentage change (e.g., in stock prices). Rather than simply using the same verbs again and again, we present a principled data-driven approach to this problem based on Shannon's noisy-channel model so as to bring variation and naturalness into the generated text. Our experiments on three large-scale real-world news corpora demonstrate that the proposed probabilistic model can be learned to accurately imitate human authors' pattern of usage around verbs, outperforming the state-of-the-art method significantly.

    Metadata

    Item Type: Article
    School: Birkbeck Schools and Departments > School of Business, Economics & Informatics > Computer Science and Information Systems
    Research Centre: Birkbeck Knowledge Lab
    Depositing User: Dr Dell Zhang
    Date Deposited: 23 Jul 2018 09:45
    Last Modified: 07 Oct 2019 18:01
    URI: http://eprints.bbk.ac.uk/id/eprint/23242

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