| Title | INTERACTION: A Generative XAI Framework for Natural Language Inference Explanations |
| Publication Type | Conference Paper |
| Year of Publication | 2022 |
| Authors | Yu, Jialin, Cristea, Alexandra I., Harit, Anoushka, Sun, Zhongtian, Aduragba, Olanrewaju Tahir, Shi, Lei, Moubayed, Noura Al |
| Conference Name | 2022 International Joint Conference on Neural Networks (IJCNN) |
| Keywords | Benchmark testing, decision making, Deep Learning, generative model, human computer interaction, natural language processing, Neural Network, Neural networks, Predictive models, pubcrawl, resilience, Resiliency, Scalability, Transformers, xai |
| Abstract | XAI with natural language processing aims to produce human-readable explanations as evidence for AI decision-making, 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; it can also generate multiple diverse explanations in step two. |
| DOI | 10.1109/IJCNN55064.2022.9892336 |
| Citation Key | yu_interaction_2022 |
INTERACTION: A Generative XAI Framework for Natural Language Inference Explanations