• 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).

Membelah Contoh
'test' 842
'train' 3.569
'validation' 781
  • Struktur fitur :
    '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
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
  • Kutipan :
        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.},