• 설명 :

레스토랑 도메인의 체코 데이터-텍스트 데이터 세트. 입력 의미 표현에는 화행 유형(알림, 확인 등), 슬롯(음식, 영역 등) 및 해당 값이 포함됩니다. Wen et al.이 English San Francisco Restaurants 데이터 세트를 번역한 것입니다. (2015).

'test' 842
'train' 3,569
'validation' 781
  • 기능 구조 :
    '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,
  • 기능 문서 :
특징 수업 모양 D타입 설명
delex_input_text 풍모Dict
delex_input_text/테이블 순서
delex_input_text/테이블/column_header 텐서
delex_input_text/테이블/콘텐츠 텐서
delex_input_text/테이블/row_number 텐서 정수16
delex_target_text 텐서
input_text 풍모Dict
입력_텍스트/테이블 순서
input_text/테이블/column_header 텐서
input_text/테이블/콘텐츠 텐서
입력_텍스트/테이블/행_번호 텐서 정수16
target_text 텐서
  • 인용 :
        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.},