cs_restoran

• Deskripsi :

Dataset data-ke-teks Ceko di domain restoran. Representasi makna input berisi jenis tindakan dialog (informasikan, konfirmasi, dll.), slot (makanan, area, dll.) dan nilainya. Itu berasal sebagai terjemahan dari dataset Restoran Inggris San Francisco oleh Wen et al. (2015).

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

• Kode sumber : tfds.structured.cs_restaurants.CSRestaurants

• Versi :

  • 1.0.0 (default): Tidak ada catatan rilis.

• Ukuran unduhan : 1.40 MiB

• Ukuran dataset : 2.46 MiB

• Di-cache otomatis ( dokumentasi ): Ya

• Perpecahan :

Membelah	Contoh
'test'	842
'train'	3.569
'validation'	781

• Struktur fitur :

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,
})

• Dokumentasi fitur :

Fitur	Kelas	Membentuk	Dtype	Keterangan
	fiturDict
delex_input_text	fiturDict
delex_input_text/tabel	Urutan
delex_input_text/table/column_header	Tensor		rangkaian
delex_input_text/table/content	Tensor		rangkaian
delex_input_text/table/row_number	Tensor		int16
delex_target_text	Tensor		rangkaian
Masukkan teks	fiturDict
masukan_teks/tabel	Urutan
input_text/table/column_header	Tensor		rangkaian
input_teks/tabel/konten	Tensor		rangkaian
input_teks/tabel/nomor_baris	Tensor		int16
target_text	Tensor		rangkaian

• Kunci yang diawasi (Lihat as_supervised doc ): ('input_text', 'target_text')

• Gambar ( tfds.show_examples ): Tidak didukung.

• Contoh ( tfds.as_dataframe ):

• Kutipan :

@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.},
}