Reference-guided Style-Consistent Content Transfer
- authored by
- Wei Fan Chen, Milad Alshomary, Maja Stahl, Khalid Al Khatib, Benno Stein, Henning Wachsmuth
- Abstract
In this paper, we introduce the task of style-consistent content transfer, which concerns modifying a text's content based on a provided reference statement while preserving its original style. We approach the task by employing multi-task learning to ensure that the modified text meets three important conditions: reference faithfulness, style adherence, and coherence. In particular, we train three independent classifiers for each condition. During inference, these classifiers are used to determine the best modified text variant. Our evaluation, conducted on hotel reviews and news articles, compares our approach with sequence-to-sequence and error correction baselines. The results demonstrate that our approach reasonably generates text satisfying all three conditions. In subsequent analyses, we highlight the strengths and limitations of our approach, providing valuable insights for future research directions.
- Organisation(s)
-
Natural Language Processing Section
Institute of Artificial Intelligence
- External Organisation(s)
-
University of Bonn
University of Groningen
Bauhaus-Universität Weimar
- Type
- Conference contribution
- Pages
- 13754-13768
- No. of pages
- 15
- Publication date
- 2024
- Publication status
- Published
- Peer reviewed
- Yes
- ASJC Scopus subject areas
- Theoretical Computer Science, Computational Theory and Mathematics, Computer Science Applications