cs_restaurants

• Descriptif :

Ensemble de données tchèques data-to-text dans le domaine de la restauration. Les représentations de sens d'entrée contiennent un type d'acte de dialogue (informer, confirmer, etc.), des créneaux (nourriture, zone, etc.) et leurs valeurs. Il est né d'une traduction de l'ensemble de données anglais San Francisco Restaurants par Wen et al. (2015).

• Page d' accueil : https://github.com/UFAL-DSG/cs_restaurant_dataset

• Code source : tfds.structured.cs_restaurants.CSRestaurants

• Versions :

  • 1.0.0 (par défaut) : aucune note de version.

• Taille du téléchargement : 1.40 MiB

• Taille du jeu de données : 2.46 MiB

• Mise en cache automatique ( documentation ): Oui

• Fractionnements :

• Structure des fonctionnalités :

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,
})

• Documentation des fonctionnalités :

• Clés supervisées (Voir as_supervised doc ): ('input_text', 'target_text')

• Figure ( tfds.show_examples ) : non pris en charge.

• Exemples ( tfds.as_dataframe ):

• Citation :

@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.},
}