e2e_nettoyé

  • Description:

Une version de mise à jour des données E2E NLG Challenge avec des MR nettoyés. Les données E2E contiennent une représentation du sens basée sur les actes de dialogue (MR) dans le domaine de la restauration et jusqu'à 5 références en langage naturel, ce qu'il faut prédire.

Diviser Exemples
'test' 4 693
'train' 33 525
'validation' 4 299
  • Caractéristiques:
FeaturesDict({
    'input_text': FeaturesDict({
        'table': Sequence({
            'column_header': tf.string,
            'content': tf.string,
            'row_number': tf.int16,
        }),
    }),
    'target_text': tf.string,
})
  • citation:
@inproceedings{dusek-etal-2019-semantic,
    title = "Semantic Noise Matters for Neural Natural Language Generation",
    author = "Du{\v{s} }ek, Ond{\v{r} }ej  and
      Howcroft, David M.  and
      Rieser, Verena",
    booktitle = "Proceedings of the 12th International Conference on Natural Language Generation",
    month = oct # "{--}" # nov,
    year = "2019",
    address = "Tokyo, Japan",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-8652",
    doi = "10.18653/v1/W19-8652",
    pages = "421--426",
    abstract = "Neural natural language generation (NNLG) systems are known for their pathological outputs, i.e. generating text which is unrelated to the input specification. In this paper, we show the impact of semantic noise on state-of-the-art NNLG models which implement different semantic control mechanisms. We find that cleaned data can improve semantic correctness by up to 97{\%}, while maintaining fluency. We also find that the most common error is omitting information, rather than hallucination.",
}