State-of-the-art automated essay scoring engines such as e-rater do not grade essay content, focusing instead on providing diagnostic trait feedback on categories such as grammar, usage, mechanics, style and organization. Content-based essay scoring is very challenging: it requires an understanding of essay content and is beyond the reach of today's automated essay scoring technologies. As a result, content-dependent dimensionsof essay quality are largely ignored in existing automated essay scoring research. In this talk, we describe our recent and ongoing efforts on content-based essay scoring, sharing the lessons we learned from automatically scoring one of the arguably most important content-dependent dimensions of persuasive essay quality, argument persuasiveness.
Vincent Ng is a Professor in the Computer Science Department at the University of Texas at Dallas. He is also the director of the Machine Learning and Language Processing Laboratory in the Human Language Technology Research Institute at UT Dallas. He obtained his B.S. from Carnegie Mellon University and his Ph.D. from Cornell University. His research is in the area of Natural Language Processing, focusing on the development of computational methods for addressing key tasks in information extraction and discourse processing.
Faculty Host: Yulia Tsvetkov
Refreshments: 4:00 LTI 5th Floor Kitchenette