INTERACTION: A Generative XAI Framework for Natural Language Inference Explanations

Yu, Jialin; Cristea, Alexandra I.; Harit, Anoushka; Sun, Zhongtian; Aduragba, Olanrewaju Tahir; Shi, Lei; Al Moubayed, Noura

Abstract

XAI with natural language processing aims to produce human-readable explanations as evidence for AI decisionmaking, which addresses explainability and transparency. However, from an HCI perspective, the current approaches only focus on delivering a single explanation, which fails to account for the diversity of human thoughts and experiences in language. This paper thus addresses this gap, by proposing a generative XAI framework, INTERACTION (explaIn aNd predicT thEn queRy with contextuAl CondiTional varIational autO-eNcoder). Our novel framework presents explanation in two steps: (step one) Explanation and Label Prediction; and (step two) Diverse Evidence Generation. We conduct intensive experiments with the Transformer architecture on a benchmark dataset, e-SNLI [1]. Our method achieves competitive or better performance against state-of-the-art baseline models on explanation generation (up to 4.7% gain in BLEU) and prediction (up to 4.4% gain in accuracy) in step one

Citation

Yu, J., Cristea, A. I., Harit, A., Sun, Z., Aduragba, O. T., Shi, L., & Al Moubayed, N. (2022, July). INTERACTION: A Generative XAI Framework for Natural Language Inference Explanations. Presented at 2022 International Joint Conference on Neural Networks (IJCNN), Padova, Italy

• Presentation Conference Type: Conference Paper (published)
• Conference Name: 2022 International Joint Conference on Neural Networks (IJCNN)
• Start Date: Jul 18, 2022
• End Date: Jul 23, 2022
• Acceptance Date: Apr 26, 2022
• Online Publication Date: Sep 30, 2022
• Publication Date: 2022
• Deposit Date: Aug 31, 2022
• Publicly Available Date: Sep 1, 2022
• Series ISSN: 2161-4393,2161-4407
• DOI: https://doi.org/10.1109/ijcnn55064.2022.9892336
• Public URL: https://durham-repository.worktribe.com/output/1137091