@inproceedings{zeidler-roth-2026-explicit,
    title = "Can You Be More Explicit? A Task and Dataset on Explicitations of Implicit Meaning",
    author = "Zeidler, Laura  and
      Roth, Michael",
    editor = "Mohammad, Saif M.  and
      Ousidhoum, Nedjma",
    booktitle = "Proceedings of the 15th Joint Conference on Lexical and Computational Semantics (*{SEM} 2026)",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl-workshops/2026.starsem-conference.12/",
    pages = "178--197",
    ISBN = "979-8-89176-413-2",
    abstract = "Making texts clear and comprehensible has become an increasingly important topic in NLP. A possible strategy to enhance text comprehension is to make implicitly conveyed meaning explicit. To explore the role of explicit vs. implied meaning, we study cases of so-called explicitations, i.e. revisions of text in which implicitly conveyed content is made explicit. Using revision histories from wikiHow, we propose a rule-based approach to extract candidate explicitations and curate a human-annotated dataset in which explicitations are distinguished from insertions of new information. Our analyses show that while the extraction method is effective in retrieving relevant cases, distinguishing explicitations from new information is a challenging and often subjective task, reflecting differences in background knowledge and reasoning. Experimentally, we find off-the-shelf LLMs to achieve promising performance, with inconsistent gains from few-shot prompting and fine-tuning. In contrast, fine-tuned NLI models benefit consistently from supervised training and show stronger robustness under distribution shift. In sum, our findings show that the task is challenging, but also indicate that our annotated dataset contains informative signals that models can learn from, paving the way for further research on explicitations."
}