  Logical Table-to-Text (LT2T) generation is tasked with generating logically
faithful sentences from tables. There currently exists two challenges in the
field: 1) Faithfulness: how to generate sentences that are factually correct
given the table content; 2) Diversity: how to generate multiple sentences that
offer different perspectives on the table. This work proposes LoFT, which
utilizes logic forms as fact verifiers and content planners to control LT2T
generation. Experimental results on the LogicNLG dataset demonstrate that LoFT
is the first model that addresses unfaithfulness and lack of diversity issues
simultaneously. Our code is publicly available at
https://github.com/Yale-LILY/LoFT.
