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        <title>Probabilistic Verb Selection for Data-to-Text Generation</title>
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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.</abstract>
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    <identifier type="doi">10.1162/tacl_a_00038</identifier>
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        <date>2018</date>
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%0 Journal Article
%T Probabilistic Verb Selection for Data-to-Text Generation
%A Zhang, Dell
%A Yuan, Jiahao
%A Wang, Xiaoling
%A Foster, Adam
%J Transactions of the Association for Computational Linguistics
%D 2018
%V 6
%I MIT Press
%C Cambridge, MA
%F zhang-etal-2018-probabilistic
%X 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.
%R 10.1162/tacl_a_00038
%U https://aclanthology.org/Q18-1038
%U https://doi.org/10.1162/tacl_a_00038
%P 511-527

Markdown (Informal)

[Probabilistic Verb Selection for Data-to-Text Generation](https://aclanthology.org/Q18-1038) (Zhang et al., TACL 2018)

• Probabilistic Verb Selection for Data-to-Text Generation (Zhang et al., TACL 2018)

ACL

• Dell Zhang, Jiahao Yuan, Xiaoling Wang, and Adam Foster. 2018. Probabilistic Verb Selection for Data-to-Text Generation. Transactions of the Association for Computational Linguistics, 6:511–527.