لدي سؤال؟ تواصل مع المجتمع في منتدى زيارة منتدى TensorFlow

e2e_cleaned

  • الوصف :

إصدار محدث لبيانات تحدي E2E NLG مع MRs نظيف. تحتوي بيانات E2E على تمثيل المعنى القائم على الحوار (MR) في مجال المطعم وما يصل إلى 5 مراجع في اللغة الطبيعية ، وهو ما يحتاج المرء للتنبؤ به.

انشق، مزق أمثلة
'test' 4،693
'train' 33525
'validation' 4299
  • الميزات :
FeaturesDict({
    'input_text': FeaturesDict({
        'table': Sequence({
            'column_header': tf.string,
            'content': tf.string,
            'row_number': tf.int16,
        }),
    }),
    'target_text': tf.string,
})
  • الاقتباس :
@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.",
}