Jenny Rose Finkel Research Statement

The field of natural language processing (NLP) is already responsible for several widely used technologies, including machine translation and automatic speech recognition, and with the rise of ubiquitous online communication, opportunities for it to influence the lives of ordinary people are expanding. The tasks that people really care about are high level, semantically-oriented ones: question answering, machine translation, machine reading, speech interfaces for robots and machines, and others that we haven’t even thought of yet. Humans are very good at these types of tasks, in part because they naturally employ holistic language processing; they effortlessly keep track of many layers of low-level information, while simultaneously integrating in long distance information from elsewhere in the conversation or document. In contrast, much NLP research focuses on lower-level tasks, like parsing, named entity recognition, and part-of-speech tagging. Moreover, for the sake of efficiency, researchers modeling these phenomena make extremely strong independence assumptions, which completely decouple these tasks, and only look at local context when making decisions. My research has addressed just this deficiency, producing systems which jointly process different levels of information, and which globally optimize over entire documents instead of small subregions, producing analyses which are more consistent, of higher quality, and generally more useful for doing the kinds of tasks that non-researchers actually care about.