Text Fact Transfer
Nishant Balepur Jie Huang Kevin Chen-Chuan Chang
Text style transfer is a prominent task that aims to control the style of
text without inherently changing its factual content. To cover more text
modification applications, such as adapting past news for current events and
repurposing educational materials, we propose the task of text fact transfer,
which seeks to transfer the factual content of a source text between topics
without modifying its style. We find that existing language models struggle
with text fact transfer, due to their inability to preserve the specificity and
phrasing of the source text, and tendency to hallucinate errors. To address
these issues, we design ModQGA, a framework that minimally modifies a source
text with a novel combination of end-to-end question generation and
specificity-aware question answering. Through experiments on four existing
datasets adapted for text fact transfer, we show that ModQGA can accurately
transfer factual content without sacrificing the style of the source text.