Papers with Few-shot setting

1 papers
Logic2Text: High-Fidelity Natural Language Generation from Logical Forms (2020.findings-emnlp)

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Challenge: Recent studies on Natural Language Generation (NLG) from structured data focus on surface descriptions of simple record sequences, for example, attribute-value pairs of fixed or very limited schema.
Approach: They propose to use a large-scale dataset to generate NLG from logical forms to obtain controllable and faithful generations from structured data.
Outcome: The proposed model can describe interesting facts from logical inferences across records, but it is difficult to produce such fidelity.

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