Research Output

Finding middle ground? Multi-objective Natural Language Generation from time-series data

  A Natural Language Generation (NLG) system is able to generate text from nonlinguistic data, ideally personalising the content to a user’s specific needs. In some cases, however, there are multiple stakeholders with their own individual goals, needs and preferences. In this paper, we explore the feasibility of combining the preferences of two different user groups, lecturers and students, when generating
summaries in the context of student feedback generation. The preferences of each user group are modelled as a multivariate
optimisation function, therefore the task of generation is seen as a multi-objective (MO) optimisation task, where the two functions are combined into one. This initial study shows that treating the preferences of each user group equally smooths the weights of the MO function, in a way that preferred content of the user groups is
not presented in the generated summary.

  • Date:

    31 December 2014

  • Publication Status:

    Published

  • Publisher

    Association for Computational Linguistics (ACL)

  • DOI:

    10.3115/v1/e14-4041

  • Library of Congress:

    QA75 Electronic computers. Computer science

  • Dewey Decimal Classification:

    000 Computer science, information & general works

Citation

Gkatzia, D., Hastie, H., & Lemon, O. (2014). Finding middle ground? Multi-objective Natural Language Generation from time-series data. In Proceedings of the 14th Conference of the European Chapter of the Association for Computational Linguistics, volume 2: Short Papersdoi:10.3115/v1/e14-4041

Authors

Keywords

Natural Language Generation (NGL), Lecturers, Students, User Specific Needs,

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