• توضیحات :

انتشار به‌روزرسانی داده‌های چالش E2E NLG با MRهای تمیز شده. داده‌های E2E شامل بازنمایی معنا مبتنی بر گفت‌وگو (MR) در حوزه رستوران و حداکثر 5 مرجع به زبان طبیعی است که باید پیش‌بینی کرد.

شکاف مثال ها
'test' 4693
'train' 33,525
'validation' 4299
  • ساختار ویژگی :
    'input_text': FeaturesDict({
        'table': Sequence({
            'column_header': string,
            'content': string,
            'row_number': int16,
    'target_text': string,
  • مستندات ویژگی :
ویژگی کلاس شکل نوع D شرح
متن ورودی FeaturesDict
input_text/table توالی
input_text/table/column_header تانسور رشته
input_text/table/content تانسور رشته
input_text/table/row_number تانسور int16
هدف_متن تانسور رشته
  • نقل قول :
    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.",