2020

Logical Natural Language Generation from Open-Domain Tables

Chen, Wenhu, Chen, Jianshu, Su, Yu et al.

Understand

Neural natural language generation (NLG) models have recently shown remarkable progress in fluency and coherence.

However, existing studies on neural NLG are primarily focused on surface-level realizations with limited emphasis on logical inference, an important aspect of human thinking and language.
In this paper, we suggest a new NLG task where a model is tasked with generating natural language statements that can be \emph{logically entailed} by the facts in an open-domain semi-structured table.
To facilitate the study of the proposed logical NLG problem, we use the existing TabFact dataset \cite{chen2019tabfact} featured with a wide range of logical/symbolic inferences as our testbed, and propose new automatic metrics to evaluate the fidelity of generation models w.r.t.\ logical inference.

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