@inproceedings{chen-etal-2024-reference,
    title = "Reference-guided Style-Consistent Content Transfer",
    author = "Chen, Wei-Fan  and
      Alshomary, Milad  and
      Stahl, Maja  and
      Al Khatib, Khalid  and
      Stein, Benno  and
      Wachsmuth, Henning",
    editor = "Calzolari, Nicoletta  and
      Kan, Min-Yen  and
      Hoste, Veronique  and
      Lenci, Alessandro  and
      Sakti, Sakriani  and
      Xue, Nianwen",
    booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)",
    month = may,
    year = "2024",
    address = "Torino, Italia",
    publisher = "ELRA and ICCL",
    url = "https://aclanthology.org/2024.lrec-main.1201",
    pages = "13754--13768",
    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.",
}

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.
Chen, Wei-Fan Alshomary, Milad Stahl, Maja Al Khatib, Khalid Stein, Benno Wachsmuth, Henning
Reference-guided Style-Consistent Content Transfer