@inproceedings{pacheco-etal-2022-hands,
    title = "Hands-On Interactive Neuro-Symbolic {NLP} with {DR}ai{L}",
    author = "Pacheco, Maria Leonor  and
      Roy, Shamik  and
      Goldwasser, Dan",
    editor = "Che, Wanxiang  and
      Shutova, Ekaterina",
    booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing: System Demonstrations",
    month = dec,
    year = "2022",
    address = "Abu Dhabi, UAE",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/add-emnlp-2024-awards/2022.emnlp-demos.37/",
    doi = "10.18653/v1/2022.emnlp-demos.37",
    pages = "371--378",
    abstract = "We recently introduced DRaiL, a declarative neural-symbolic modeling framework designed to support a wide variety of NLP scenarios. In this paper, we enhance DRaiL with an easy to use Python interface, equipped with methods to define, modify and augment DRaiL models interactively, as well as with methods to debug and visualize the predictions made. We demonstrate this interface with a challenging NLP task: predicting sentence and entity level moral sentiment in political tweets."
}