@inproceedings{guzman-etal-2024-towards,
    title = "Towards Explainable Multi-Label Text Classification: A Multi-Task Rationalisation Framework for Identifying Indicators of Forced Labour",
    author = "Guzman, Erick Mendez  and
      Schlegel, Viktor  and
      Batista-Navarro, Riza",
    editor = "Dementieva, Daryna  and
      Ignat, Oana  and
      Jin, Zhijing  and
      Mihalcea, Rada  and
      Piatti, Giorgio  and
      Tetreault, Joel  and
      Wilson, Steven  and
      Zhao, Jieyu",
    booktitle = "Proceedings of the Third Workshop on NLP for Positive Impact",
    month = nov,
    year = "2024",
    address = "Miami, Florida, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-emnlp/2024.nlp4pi-1.8/",
    doi = "10.18653/v1/2024.nlp4pi-1.8",
    pages = "98--112",
    abstract = "The importance of rationales, or natural language explanations, lies in their capacity to bridge the gap between machine predictions and human understanding, by providing human-readable insights into why a text classifier makes specific decisions. This paper presents a novel multi-task rationalisation approach tailored to enhancing the explainability of multi-label text classifiers to identify indicators of forced labour. Our framework integrates a rationale extraction task with the classification objective and allows the inclusion of human explanations during training. We conduct extensive experiments using transformer-based models on a dataset consisting of 2,800 news articles, each annotated with labels and human-generated explanations. Our findings reveal a statistically significant difference between the best-performing architecture leveraging human rationales during training and variants using only labels. Specifically, the supervised model demonstrates a 10{\%} improvement in predictive performance measured by the weighted F1 score, a 15{\%} increase in the agreement between human and machine-generated rationales, and a 4{\%} improvement in the generated rationales' comprehensiveness. These results hold promising implications for addressing complex human rights issues with greater transparency and accountability using advanced NLP techniques."
}