@article{gardent-perez-beltrachini-2017-statistical,
    title = "A Statistical, Grammar-Based Approach to Microplanning",
    author = "Gardent, Claire  and
      Perez-Beltrachini, Laura",
    journal = "Computational Linguistics",
    volume = "43",
    number = "1",
    month = apr,
    year = "2017",
    address = "Cambridge, MA",
    publisher = "MIT Press",
    url = "https://preview.aclanthology.org/build-pipeline-with-new-library/J17-1001/",
    doi = "10.1162/COLI_a_00273",
    pages = "1--30",
    abstract = "Although there has been much work in recent years on data-driven natural language generation, little attention has been paid to the fine-grained interactions that arise during microplanning between aggregation, surface realization, and sentence segmentation. In this article, we propose a hybrid symbolic/statistical approach to jointly model the constraints regulating these interactions. Our approach integrates a small handwritten grammar, a statistical hypertagger, and a surface realization algorithm. It is applied to the verbalization of knowledge base queries and tested on 13 knowledge bases to demonstrate domain independence. We evaluate our approach in several ways. A quantitative analysis shows that the hybrid approach outperforms a purely symbolic approach in terms of both speed and coverage. Results from a human study indicate that users find the output of this hybrid statistic/symbolic system more fluent than both a template-based and a purely symbolic grammar-based approach. Finally, we illustrate by means of examples that our approach can account for various factors impacting aggregation, sentence segmentation, and surface realization."
}