Title: Toward Unsupervised Text Content Manipulation,
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 is unsupervised due to lack of
parallel data in practice, 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 a dataset from a basketball game
report corpus as our testbed, and develop a neural method with unsupervised
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.