BLEU: 67.08METEOR: 48.34FactSpotter: 99.09
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Data-to-Text Generation
A classic problem in natural-language generation (NLG) involves taking structured data, such as a table, as input, and producing text that adequately and fluently describes this data as output. Unlike machine translation, which aims for complete transduction of the sentence to be translated, this form of NLG is usually taken to require addressing (at least) two separate challenges: what to say, the selection of an appropriate subset of the input data to discuss, and how to say it, the surface realization of a generation.
<span style="color:grey; opacity: 0.6">( Image credit: Data-to-Text Generation with Content Selection and Planning )</span>
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Dataset기록된 모델 예시MetricPaperCode
데이터셋
44개논문
BLEU: 67.32
BLEU: 64.73METEOR: 45.53Number of parameters (M): 6.2
BLEU: 65.4
BLEU: 65.05
BLEU: 66.07
BLEU: 64.7
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