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"Natural Language Understanding with Incidental Supervision"

Abstract: The fundamental issue underlying natural language understanding is that of semantics – there is a need to move toward understanding natural language at an appropriate level of abstraction, beyond the word level, in order to support knowledge extraction, natural language understanding, and communication. Machine Learning and Inference methods have become ubiquitous in our attempt to induce semantic representations of natural language and support decisions that depend on it. However, learning models for these tasks are difficult, partly since generating supervision signals for it is costly and does not scale. Consequently, making natural language understanding decisions, which typically depend on multiple, interdependent, models, becomes even more challenging.

I will describe some of our research on developing machine learning and inference methods in pursuit of understanding natural language text. I will point to some of the key challenges and some possible directions for studying this problem from a principled perspective and focus on identifying and using incidental supervision signals.


