About: Transcribing structured data into natural language descriptions has emerged as a challenging task, referred to as “data-to-text”. These structures generally regroup multiple elements, as well as their attributes. Most attempts rely on translation encoder-decoder methods which linearize elements into a sequence. This however loses most of the structure contained in the data. In this work, we propose to overpass this limitation with a hierarchical model that encodes the data-structure at the element-level and the structure level. Evaluations on RotoWire show the effectiveness of our model w.r.t. qualitative and quantitative metrics.   Goto Sponge  NotDistinct  Permalink

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  • Transcribing structured data into natural language descriptions has emerged as a challenging task, referred to as “data-to-text”. These structures generally regroup multiple elements, as well as their attributes. Most attempts rely on translation encoder-decoder methods which linearize elements into a sequence. This however loses most of the structure contained in the data. In this work, we propose to overpass this limitation with a hierarchical model that encodes the data-structure at the element-level and the structure level. Evaluations on RotoWire show the effectiveness of our model w.r.t. qualitative and quantitative metrics.
Subject
  • Language
  • Structure
  • Measurement
  • Patterns
  • Hierarchy
  • Neuropsychological assessment
  • Philosophy of language
  • Scientific method
  • Statistical data types
  • Natural language processing
  • Data structures
  • Cognitive science
  • Political culture
  • Philosophical logic
  • Internet properties established in 1997
  • Companies based in Madison, Wisconsin
  • Daily fantasy sports
  • Fantasy sports websites
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