Searching Efficiently for Solutions in Large-Scale NLP Application – Daniel Marcu (Language Weaver / ISI)
As the Natural Language Processing (NLP) and Machine Learning fields mature, the gap between the mathematical equations we write when we model a problem statistically and the manner in which we implement these equations in NLP applications is widening. In this talk, I review first some of the challenges that we face when searching for best solutions in large-scale statistical applications, such as machine translation, and the effect that the ignoring of these challenges is having on end-to-end results. I also present recent developments that have the potential to impact positively a wide range of applications where parameter estimation and search are critical.
Daniel Marcu is the Chief Technology Officer of Language Weaver Inc. and an Associate Professor and Project Leader at the Information Sciences Institute, University of Southern California. His published work includes an MIT Press book, “The Theory and Practice of Discourse Parsing and Summarization”, and best paper awards, with his ISI colleagues, at AAAI-2000 and ACL-2001 for research on statistical-based summarization and translation. His research has influenced a diverse range of natural language processing fields from discourse parsing to summarization, machine translation, and question answering. His current focus is on efficient learning and decoding/search for statistical machine translation applications.