  Logical table-to-text generation is a task that involves generating logically
faithful sentences from tables, which requires models to derive logical level
facts from table records via logical inference. It raises a new challenge on
the logical-level content planning of table-to-text models. However, directly
learning the logical inference knowledge from table-text pairs is very
difficult for neural models because of the ambiguity of natural language and
the scarcity of parallel data. Hence even large-scale pre-trained language
models present low logical fidelity on logical table-to-text. In this work, we
propose a PLOG (Pretrained Logical Form Generator) framework to improve the
generation fidelity. Specifically, PLOG is first pretrained on a
table-to-logic-form generation (table-to-logic) task, then finetuned on
downstream table-to-text tasks. The formal definition of logical forms enables
us to collect large amount of accurate logical forms from tables without human
annotation. In addition, PLOG can learn logical inference from table-logic
pairs much more definitely than from table-text pairs. To evaluate our model,
we further collect a controlled logical table-to-text dataset CONTLOG based on
an existing dataset. On two benchmarks, LOGICNLG and CONTLOG, PLOG outperforms
strong baselines by a large margin on the logical fidelity, demonstrating the
effectiveness of table-to-logic pretraining.
