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Talk Title: Towards Interpretable Machine Learning by Human Knowledge Reasoning

Talk Abstract: Given the great success achieved by statistical learning theories for building intelligent systems, there is still a long-standing challenge of artificial intelligence, which is to bridge the gaps between what machines know, what humans think what machines know, and what humans know, about the real world. By doing so, we are expected to ground the prior knowledge of machines to human knowledge first and perform explicit reasoning for various downstream tasks for better interpretable machine learning. 

In this talk, I will briefly present two pieces of my existing work that leverage human expert and commonsense knowledge reasoning to increase the interpretability and transparency of machine learning models in the field of natural language processing. Firstly, I will show how existing cognitive theories on human memory can inspire an interpretable framework for rationalizing the medical relation prediction task based on expert knowledge. Secondly, I will introduce how we can learn better word representations based on commonsense knowledge and reasoning. Our proposed framework learns a commonsense reasoning module guided by a self-supervision task and provides word pair and single word representations distilled from learned reasoning modules. Both the above works are able to offer reasoning paths to justify their decisions and boost the model interpretability that humans can understand with minimal knowledge barriers.

Bio: Zhen Wang is a Ph.D. student in the Department of Computer Science and Engineering at the Ohio State University advised by Prof. Huan Sun. His research centers on natural language processing, data mining, and machine learning with emphasis on information extraction, question answering, graph learning, text understanding, and interpretable machine learning. Particularly, he is interested in improving the trustworthiness and generalizability of data-driven machine learning models by interpretable and robust knowledge representation and reasoning. He has published papers in several top-tier data science conferences, such as KDD, ACL, WSDM as well as journals like Bioinformatics. He conducts interdisciplinary research that connects artificial intelligence with cognitive neuroscience, linguistics, software engineering, and medical informatics, etc.