cs_restaurants

• Descrição :

Conjunto de dados tcheco de dados para texto no domínio do restaurante. As representações de significado de entrada contêm um tipo de ato de diálogo (informar, confirmar etc.), slots (comida, área etc.) e seus valores. Originou-se como uma tradução do conjunto de dados English San Francisco Restaurants de Wen et al. (2015).

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

• Código -fonte: tfds.structured.cs_restaurants.CSRestaurants

• Versões :

  • 1.0.0 (padrão): sem notas de versão.

• Tamanho do download : 1.40 MiB

• Tamanho do conjunto de dados : 2.46 MiB

• Cache automático ( documentação ): Sim

• Divisões :

• Estrutura de recursos :

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,
})

• Documentação do recurso:

• Chaves supervisionadas (consulte o documento as_supervised ): ('input_text', 'target_text')

• Figura ( tfds.show_examples ): Não suportado.

• Exemplos ( tfds.as_dataframe ):

• Citação :

@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.},
}