cs_restaurants

• 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).

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

• 소스 코드 : tfds.structured.cs_restaurants.CSRestaurants

• 버전 :

  • 1.0.0 (기본값): 릴리스 정보가 없습니다.

• 다운로드 크기 : 1.40 MiB

• 데이터 세트 크기 : 2.46 MiB

• 자동 캐시 ( 문서 ): 예

• 분할 :

• 기능 구조 :

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,
})

• 기능 문서 :

• 감독 키 ( as_supervised 문서 참조): ('input_text', 'target_text')

• 그림 ( tfds.show_examples ): 지원되지 않습니다.

• 예 ( tfds.as_dataframe ):

• 인용 :

@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.},
}