• 설명 :

정리된 MR이 포함된 E2E NLG Challenge 데이터의 업데이트 릴리스입니다. E2E 데이터는 레스토랑 도메인의 화행 기반 MR(의미 표현)과 예측해야 하는 자연어 참조 최대 5개를 포함합니다.

'test' 4,693
'train' 33,525
'validation' 4,299
  • 기능 구조 :
    'input_text': FeaturesDict({
        'table': Sequence({
            'column_header': string,
            'content': string,
            'row_number': int16,
    'target_text': string,
  • 기능 문서 :
특징 수업 모양 D타입 설명
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 :
    title = "Semantic Noise Matters for Neural Natural Language Generation",
    author = "Du{\v{s} }ek, Ond{\v{r} }ej  and
      Howcroft, David M.  and
      Rieser, Verena",
    booktitle = "Proceedings of the 12th International Conference on Natural Language Generation",
    month = oct # "{--}" # nov,
    year = "2019",
    address = "Tokyo, Japan",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-8652",
    doi = "10.18653/v1/W19-8652",
    pages = "421--426",
    abstract = "Neural natural language generation (NNLG) systems are known for their pathological outputs, i.e. generating text which is unrelated to the input specification. In this paper, we show the impact of semantic noise on state-of-the-art NNLG models which implement different semantic control mechanisms. We find that cleaned data can improve semantic correctness by up to 97{\%}, while maintaining fluency. We also find that the most common error is omitting information, rather than hallucination.",