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- W4300577188 abstract "Recently, several data-sets associating data to text have been created to train data-to-text surface realisers. It is unclear however to what extent the surface realisation task exercised by these data-sets is linguistically challenging. Do these data-sets provide enough variety to encourage the development of generic, high-quality data-to-text surface realisers ? In this paper, we argue that these data-sets have important drawbacks. We back up our claim using statistics, metrics and manual evaluation. We conclude by eliciting a set of criteria for the creation of a data-to-text benchmark which could help better support the development, evaluation and comparison of linguistically sophisticated data-to-text surface realisers." @default.
- W4300577188 created "2022-10-03" @default.
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- W4300577188 date "2017-09-04" @default.
- W4300577188 modified "2023-09-27" @default.
- W4300577188 title "Analysing Data-To-Text Generation Benchmarks" @default.
- W4300577188 hasPublicationYear "2017" @default.
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