@inproceedings{sun-etal-2023-incorporating,
    title = "Incorporating Task-Specific Concept Knowledge into Script Learning",
    author = "Sun, Chenkai  and
      Xu, Tie  and
      Zhai, ChengXiang  and
      Ji, Heng",
    editor = "Vlachos, Andreas  and
      Augenstein, Isabelle",
    booktitle = "Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics",
    month = may,
    year = "2023",
    address = "Dubrovnik, Croatia",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/Ingest-2025-COMPUTEL/2023.eacl-main.220/",
    doi = "10.18653/v1/2023.eacl-main.220",
    pages = "3026--3040",
    abstract = "In this paper, we present Tetris, a new task of Goal-Oriented Script Completion. Unlike previous work, it considers a more realistic and general setting, where the input includes not only the goal but also additional user context, including preferences and history. To address this problem, we propose a novel approach, which uses two techniques to improve performance: (1) concept prompting, and (2) script-oriented contrastive learning that addresses step repetition and hallucination problems. On our WikiHow-based dataset, we find that both methods improve performance."
}