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  • 标题:Ranking Algorithms for Word Ordering in Surface Realization
  • 本地全文:下载
  • 作者:Alessandro Mazzei ; Mattia Cerrato ; Roberto Esposito
  • 期刊名称:Information
  • 电子版ISSN:2078-2489
  • 出版年度:2021
  • 卷号:12
  • 期号:8
  • 页码:337
  • DOI:10.3390/info12080337
  • 语种:English
  • 出版社:MDPI Publishing
  • 摘要:In natural language generation, word ordering is the task of putting the words composing the output surface form in the correct grammatical order. In this paper, we propose to apply general learning-to-rank algorithms to the task of word ordering in the broader context of surface realization. The major contributions of this paper are: (i) the design of three deep neural architectures implementing pointwise, pairwise, and listwise approaches for ranking; (ii) the testing of these neural architectures on a surface realization benchmark in five natural languages belonging to different typological families. The results of our experiments show promising results, in particular highlighting the performance of the pairwise approach, paving the way for a more transparent surface realization from arbitrary tree- and graph-like structures.
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