Computer Science Seminar: Adam Poliak (Johns Hopkins University)

Speaker: Adam Poliak, Johns Hopkins University
Title: Exploring Reasoning Capabilities in Natural Language Processing Models

Natural Language Processing (NLP) is the field of building machines that humans can seamlessly interact with through spoken and written language.  As these machines become more ubiquitous in our daily lives through technologies like Google Translate and Apple’s Siri, it is important to develop methods that provide insight into understanding the reasoning capabilities of these systems. We will discuss my work on developing diagnostic test-suites composed of fine-grained semantic phenomena. I will demonstrate how to use these tests to explore the reasoning capabilities of contemporary NLP systems. Additionally, we will also discuss biases in prior datasets that the research community has accepted as gold standards. We will discuss how these biases limit the previous datasets’ usefulness in testing how well NLP systems successfully understand natural language. With the remaining time, we will discuss how lessons from these studies can be applied to identifying emergency needs during disaster scenarios.

Adam Poliak is a final year Ph.D. Candidate in Computer Science at Johns Hopkins University advised by Dr. Benjamin Van Durme. Adam is an affiliate of the Center for Language & Speech Processing as his research focuses on Natural Language Processing and Computational Semantics. In particular, his research analyses NLP systems and datasets to provide insight into the failures of NLP systems as well as biases in textual data. His work has been published in top-tier NLP conferences and he won Best Paper Awards in 2018 and 2019 at The Joint Conference on Lexical and Computational Semantics. Adam was a 2017 GEM Fellow and he has performed research at Bloomberg L.P. and the MIT Lincoln Laboratory.