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.- Anthology ID:
- 2024.nlp4pi-1.8
- Volume:
- Proceedings of the Third Workshop on NLP for Positive Impact
- Month:
- November
- Year:
- 2024
- Address:
- Miami, Florida, USA
- Editors:
- Daryna Dementieva, Oana Ignat, Zhijing Jin, Rada Mihalcea, Giorgio Piatti, Joel Tetreault, Steven Wilson, Jieyu Zhao
- Venue:
- NLP4PI
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 98–112
- Language:
- URL:
- https://aclanthology.org/2024.nlp4pi-1.8
- DOI:
- 10.18653/v1/2024.nlp4pi-1.8
- Cite (ACL):
- Erick Mendez Guzman, Viktor Schlegel, and Riza Batista-Navarro. 2024. Towards Explainable Multi-Label Text Classification: A Multi-Task Rationalisation Framework for Identifying Indicators of Forced Labour. In Proceedings of the Third Workshop on NLP for Positive Impact, pages 98–112, Miami, Florida, USA. Association for Computational Linguistics.
- Cite (Informal):
- Towards Explainable Multi-Label Text Classification: A Multi-Task Rationalisation Framework for Identifying Indicators of Forced Labour (Guzman et al., NLP4PI 2024)
- PDF:
- https://preview.aclanthology.org/dois-2013-emnlp/2024.nlp4pi-1.8.pdf