Abstract: As natural language processing now permeates many different applications, its practical use is unquestionable. However, at the same time NLP is still imperfect, and errors cause everything from minor inconveniences to major PR disasters. Better understanding when our NLP models work and when they fail is critical to the efficient and reliable use of NLP in real-world scenarios. So how can we do so? In this talk I will discuss two issues: automatic evaluation of generated text, and automatic fine-grained analysis of NLP system results, which are some first steps towards a science of NLP model evaluation.
Bio: Graham Neubig is an associate professor at the Language Technologies Institute of Carnegie Mellon University. His research focuses on multilingual natural language processing, natural language interfaces to computers, and machine learning methods for NLP, with the final goal of every person in the world being able to communicate with each-other, and with computers in their own language. He also contributes to making NLP research more accessible through open publishing of research papers, advanced NLP course materials and video lectures, and open-source software, all of which are available on his web site.