cs_ristoranti

• Descrizione :

Set di dati ceco da dati a testo nel dominio dei ristoranti. Le rappresentazioni del significato dell'input contengono un tipo di atto di dialogo (informare, confermare ecc.), slot (cibo, area, ecc.) ei loro valori. È nato come traduzione del set di dati inglese San Francisco Restaurants di Wen et al. (2015).

• Pagina iniziale : https://github.com/UFAL-DSG/cs_restaurant_dataset

• Codice sorgente : tfds.structured.cs_restaurants.CSRestaurants

• Versioni :

  • 1.0.0 (impostazione predefinita): nessuna nota di rilascio.

• Dimensione del download : 1.40 MiB

• Dimensione del set di dati: 2.46 MiB

• Auto-cache ( documentazione ): Sì

• Divisioni :

• Struttura delle caratteristiche :

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,
})

• Documentazione delle funzionalità :

• Chiavi supervisionate (vedi as_supervised doc ): ('input_text', 'target_text')

• Figura ( tfds.show_examples ): non supportato.

• Esempi ( tfds.as_dataframe ):

• Citazione :

@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.},
}