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 Faculties and Schools > Faculty of Science > School of Computing and Mathematical Sciences |
| Research Centres and Institutes: | Birkbeck Knowledge Lab, Data Analytics, Birkbeck Institute for |
| Depositing User: | Dell Zhang |
| Date Deposited: | 23 Jul 2018 09:45 |
| Last Modified: | 09 Aug 2023 12:44 |
| URI: | https://eprints.bbk.ac.uk/id/eprint/23242 |
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