@inproceedings{schenk-chiarcos-2017-resource,
    title = "Resource-Lean Modeling of Coherence in Commonsense Stories",
    author = "Schenk, Niko  and
      Chiarcos, Christian",
    editor = "Roth, Michael  and
      Mostafazadeh, Nasrin  and
      Chambers, Nathanael  and
      Louis, Annie",
    booktitle = "Proceedings of the 2nd Workshop on Linking Models of Lexical, Sentential and Discourse-level Semantics",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/iwcs-25-ingestion/W17-0910/",
    doi = "10.18653/v1/W17-0910",
    pages = "68--73",
    abstract = "We present a resource-lean neural recognizer for modeling coherence in commonsense stories. Our lightweight system is inspired by successful attempts to modeling discourse relations and stands out due to its simplicity and easy optimization compared to prior approaches to narrative script learning. We evaluate our approach in the Story Cloze Test demonstrating an absolute improvement in accuracy of 4.7{\%} over state-of-the-art implementations."
}