@inproceedings{ahia-etal-2023-lexplain,
    title = "{LEXPLAIN}: Improving Model Explanations via Lexicon Supervision",
    author = "Ahia, Orevaoghene  and
      Gonen, Hila  and
      Balachandran, Vidhisha  and
      Tsvetkov, Yulia  and
      Smith, Noah A.",
    editor = "Palmer, Alexis  and
      Camacho-collados, Jose",
    booktitle = "Proceedings of the 12th Joint Conference on Lexical and Computational Semantics (*SEM 2023)",
    month = jul,
    year = "2023",
    address = "Toronto, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2023.starsem-1.19",
    doi = "10.18653/v1/2023.starsem-1.19",
    pages = "207--216",
    abstract = "Model explanations that shed light on the model{'}s predictions are becoming a desired additional output of NLP models, alongside their predictions. Challenges in creating these explanations include making them trustworthy and faithful to the model{'}s predictions. In this work, we propose a novel framework for guiding model explanations by supervising them explicitly. To this end, our method, LEXplain, uses task-related lexicons to directly supervise model explanations. This approach consistently improves the model{'}s explanations without sacrificing performance on the task, as we demonstrate on sentiment analysis and toxicity detection. Our analyses show that our method also demotes spurious correlations (i.e., with respect to African American English dialect) when performing the task, improving fairness.",
}

Model explanations that shed light on the model{'}s predictions are becoming a desired additional output of NLP models, alongside their predictions. Challenges in creating these explanations include making them trustworthy and faithful to the model{'}s predictions. In this work, we propose a novel framework for guiding model explanations by supervising them explicitly. To this end, our method, LEXplain, uses task-related lexicons to directly supervise model explanations. This approach consistently improves the model{'}s explanations without sacrificing performance on the task, as we demonstrate on sentiment analysis and toxicity detection. Our analyses show that our method also demotes spurious correlations (i.e., with respect to African American English dialect) when performing the task, improving fairness.
Ahia, Orevaoghene Gonen, Hila Balach ran, Vidhisha Tsvetkov, Yulia Smith, Noah A.
LEXPLAIN: Improving Model Explanations via Lexicon Supervision