Analysing Data-To-Text Generation Benchmarks

Laura Perez-Beltrachini; Claire Gardent

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arXiv · 1705.03802

Analysing Data-To-Text Generation Benchmarks

Laura Perez-Beltrachini·Claire Gardent

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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.

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Laura Perez-Beltrachini, Claire Gardent. 2017-05-10. Analysing Data-To-Text Generation Benchmarks. https://arxiv.org/abs/1705.03802

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