The AI Seminar is a weekly meeting at the University of Alberta where researchers interested in artificial intelligence (AI) can share their research. Presenters include both local speakers from the University of Alberta and visitors from other institutions. Topics can be related in any way to artificial intelligence, from foundational theoretical work to innovative applications of AI techniques to new fields and problems.
Abstract:
This talk introduces symbolic bottleneck networks, our proposed concept for improving interpretability in neural models. We present two works: the first applies fuzzy logic for explainable phrase-level reasoning in NLI under weak supervision; the second uses a hierarchical RNN to induce phrase-level chunking structures through downstream tasks. Symbolic bottleneck network highlights an interpretability-performance trade-off in NLP.
Presenter Bio:
Zijun Wu is a PhD student in NLP and Machine Learning at the University of Alberta, supervised by Dr. Lili Mou. His research focuses on the emergence of symbolic structures in neural networks, aiming to improve model interpretability and communication. He proposed the concept of symbolic bottleneck networks and has published in ICLR and Computational Linguistics.