Referring Expression Generation Using Speaker-based Attribute Selection and Trainable Realization (ATTR)

نویسندگان

  • Giuseppe Di Fabbrizio
  • Amanda Stent
  • Srinivas Bangalore
چکیده

In the first REG competition, researchers proposed several general-purpose algorithms for attribute selection for referring expression generation. However, most of this work did not take into account: a) stylistic differences between speakers; or b) trainable surface realization approaches that combine semantic and word order information. In this paper we describe and evaluate several end-to-end referring expression generation algorithms that take into consideration speaker style and use data-driven surface realization techniques.

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تاریخ انتشار 2008