Toward Controllable Text Content Manipulation

Shuai Lin, Wentao Wang, Zhiting Hu, Zichao Yang, Xiaodan Liang, Haoran Shi, Frank Xu, Eric Xing

Abstract:

Controlled generation of text is of high practical use. Recent efforts have made impressive progress in generating or editing sentences with given textual attributes (e.g., sentiment). This work studies a new practical setting of text content manipulation. Given a structured record, such as (PLAYER: Lebron, POINTS: 20, ASSISTS: 10), and a reference sentence, such as Kobe easily dropped 30 points, we aim to generate a sentence that accurately describes the full content in the record, with the same writing style (e.g., wording, transitions) of the reference. The problem combines the characteristics of data-to-text
generation and style transfer, and is challenging to minimally yet effectively manipulate the text (by rewriting/adding/deleting text portions) to ensure fidelity to the structured content. We derive two datasets from the data-to-text task as our testbed, and develop a neural method with weakly supervised competing objectives and explicit content coverage constraints. Automatic and human evaluations show superiority of our approach over competitive methods including a strong rule-based baseline and prior approaches designed for style transfer.