cs_restaurants

• Description:

Czech data-to-text dataset in the restaurant domain. The input meaning representations contain a dialogue act type (inform, confirm etc.), slots (food, area, etc.) and their values. It originated as a translation of the English San Francisco Restaurants dataset by Wen et al. (2015).

• Homepage: https://github.com/UFAL-DSG/cs_restaurant_dataset

• Source code: tfds.structured.cs_restaurants.CSRestaurants

• Versions:

  • 1.0.0 (default): No release notes.

• Download size: 1.40 MiB

• Dataset size: 2.46 MiB

• Auto-cached (documentation): Yes

• Splits:

Split	Examples
'test'	842
'train'	3,569
'validation'	781

• Feature structure:

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,
})

• Feature documentation:

Feature	Class	Shape	Dtype	Description
	FeaturesDict
delex_input_text	FeaturesDict
delex_input_text/table	Sequence
delex_input_text/table/column_header	Tensor		string
delex_input_text/table/content	Tensor		string
delex_input_text/table/row_number	Tensor		int16
delex_target_text	Tensor		string
input_text	FeaturesDict
input_text/table	Sequence
input_text/table/column_header	Tensor		string
input_text/table/content	Tensor		string
input_text/table/row_number	Tensor		int16
target_text	Tensor		string

• Supervised keys (See as_supervised doc): ('input_text', 'target_text')

• Figure (tfds.show_examples): Not supported.

• Examples (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.},
}