cs_restaurantes

• Descripción :

Conjunto de datos de conversión de datos a texto en checo en el dominio del restaurante. Las representaciones de significado de entrada contienen un tipo de acto de diálogo (informar, confirmar, etc.), espacios (comida, área, etc.) y sus valores. Se originó como una traducción del conjunto de datos de restaurantes de San Francisco en inglés de Wen et al. (2015).

• Página de inicio: https://github.com/UFAL-DSG/cs_restaurant_dataset

• Código fuente : tfds.structured.cs_restaurants.CSRestaurants

• Versiones :

  • 1.0.0 (predeterminado): Sin notas de la versión.

• Tamaño de descarga : 1.40 MiB

• Tamaño del conjunto de datos : 2.46 MiB

• Almacenamiento automático en caché ( documentación ): Sí

• Divisiones :

• Estructura de características :

FeaturesDict({
    'delex_input_text': FeaturesDict({
        'table': Sequence({
            'column_header': string,
            'content': string,
            'row_number': int16,
        }),
    }),
    'delex_target_text': string,
    'input_text': FeaturesDict({
        'table': Sequence({
            'column_header': string,
            'content': string,
            'row_number': int16,
        }),
    }),
    'target_text': string,
})

• Documentación de características :

• Teclas supervisadas (Ver as_supervised doc ): ('input_text', 'target_text')

• Figura ( tfds.show_examples ): no compatible.

• Ejemplos ( tfds.as_dataframe ):

• Cita :

@inproceedings{dusek_neural_2019,
        author = {Dušek, Ondřej and Jurčíček, Filip},
        title = {Neural {Generation} for {Czech}: {Data} and {Baselines} },
        shorttitle = {Neural {Generation} for {Czech} },
        url = {https://www.aclweb.org/anthology/W19-8670/},
        urldate = {2019-10-18},
        booktitle = {Proceedings of the 12th {International} {Conference} on {Natural} {Language} {Generation} ({INLG} 2019)},
        month = oct,
        address = {Tokyo, Japan},
        year = {2019},
        pages = {563--574},
        abstract = {We present the first dataset targeted at end-to-end NLG in Czech in the restaurant domain, along with several strong baseline models using the sequence-to-sequence approach. While non-English NLG is under-explored in general, Czech, as a morphologically rich language, makes the task even harder: Since Czech requires inflecting named entities, delexicalization or copy mechanisms do not work out-of-the-box and lexicalizing the generated outputs is non-trivial. In our experiments, we present two different approaches to this this problem: (1) using a neural language model to select the correct inflected form while lexicalizing, (2) a two-step generation setup: our sequence-to-sequence model generates an interleaved sequence of lemmas and morphological tags, which are then inflected by a morphological generator.},
}