Logic2Text: High-Fidelity Natural Language Generation from Logical Forms
Zhiyu Chen, Wenhu Chen, Hanwen Zha, Xiyou Zhou, Yunkai Zhang, Sairam Sundaresan, William Yang Wang
Abstract
Previous studies on Natural Language Generation (NLG) from structured data have primarily focused on surface-level descriptions of record sequences. However, for complex structured data, e.g., multi-row tables, it is often desirable for an NLG system to describe interesting facts from logical inferences across records. If only provided with the table, it is hard for existing models to produce controllable and high-fidelity logical generations. In this work, we formulate highfidelity NLG as generation from logical forms in order to obtain controllable and faithful generations. We present a new large-scale dataset, LOGIC2TEXT, with 10,753 descriptions involving common logic types paired with the underlying logical forms.