{"type":"ScholarlyArticle","title":"PEACH: Pretrained-Embedding Explanation across Contextual and Hierarchical Structure","authors":[{"name":"Feiqi Cao","slug":"feiqi-cao","given":"Feiqi","family":"Cao","affiliation":"School of Computer Science , University of Sydney"},{"name":"Soyeon Caren Han","slug":"soyeon-caren-han","given":"Soyeon Caren","family":"Han","affiliation":"School of Computer Science , University of Sydney; School of Computing and Information Systems , University of Melbourne"},{"name":"Hyunsuk Chung","slug":"hyunsuk-chung","given":"Hyunsuk","family":"Chung","affiliation":""}],"abstract":"In this work, we propose a novel tree-based explanation technique, PEACH (Pretrained-embedding Explanation Across Contextual and Hierarchical Structure), that can explain how text-based documents are classified by using any pretrained contextual embeddings in a tree-based human-interpretable manner. Note that PEACH can adopt any contextual embeddings of the PLMs as a training input for the decision tree. \r\nUsing the proposed PEACH, we perform a comprehensive analysis of several contextual embeddings on nine different NLP text classification benchmarks. This analysis demonstrates the flexibility of the model by appling several PLM contextual embeddings, its attribute selections, scaling, and clustering methods. Furthermore, we show the utility of explanations by visualising the feature selection and important trend of text classification via human-interpretable word-cloud-based trees, which clearly identify model mistakes and assist in dataset debugging. Besides interpretability, PEACH outperforms or is similar to those from pretrained models. Code and Appendix are in https://github.com/adlnlp/peach.","doi":"10.24963/ijcai.2024/686","url":"https://ijcai.org/proceedings/2024/686","pdf":"https://ijcai.org/proceedings/2024/0686.pdf","markdown":"https://ijcai.org/proceedings/2024/686.md","bibtex":"https://ijcai.org/proceedings/2024/bibtex/686","volume":{"slug":"2024","title":"Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2024","url":"https://ijcai.org/proceedings/2024/","year":2024,"location":"Jeju, Korea","dates":"3-9 August 2024","editors":"Kate Larson","isbn":"978-1-956792-04-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2024"},"section":"Natural Language Processing","track":"Main Track","number":686,"pages":{"first":6207,"last":6215},"keywords":["Knowledge Representation and Reasoning: KRR: Other","Natural Language Processing: NLP: Interpretability and analysis of models for NLP"],"videos":[],"references":[{"position":1,"raw":"[Devlin et al., 2019] Jacob Devlin, Ming-Wei Chang, Ken- ton Lee, and Kristina Toutanova. 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