• Descripción :

Una versión actualizada de los datos del E2E NLG Challenge con MR limpios. Los datos E2E contienen representación de significado (MR) basada en actos de diálogo en el dominio del restaurante y hasta 5 referencias en lenguaje natural, que es lo que se necesita predecir.

Separar Ejemplos
'test' 4,693
'train' 33,525
'validation' 4,299
  • Estructura de características :
    'input_text': FeaturesDict({
        'table': Sequence({
            'column_header': tf.string,
            'content': tf.string,
            'row_number': tf.int16,
    'target_text': tf.string,
  • Documentación de características :
Rasgo Clase Forma Tipo D Descripción
texto de entrada CaracterísticasDict
entrada_texto/tabla Secuencia
texto_de_entrada/tabla/encabezado_de_columna Tensor tf.cadena
entrada_texto/tabla/contenido Tensor tf.cadena
texto_de_entrada/tabla/número_de_fila Tensor tf.int16
texto_objetivo Tensor tf.cadena
  • Cita :
    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.",