Computational Linguistics Colloquium

Thursday, 7 January, 16:15
Conference Room, Building C7 4

Constrained Conditional Models: Learning and Inference in Natural Language Understanding

Making decisions in natural language understanding tasks often involves assigning values to sets of interdependent variables where an expressive dependency structure among these can influence, or even dictate, what assignments are possible. Structured learning problems provide one such example, but we are interested in a broader setting where multiple models are involved, global inference is over these is essential, but it may not be ideal, or possible, to learn them jointly.

I will present work on Constrained Conditional Models (CCMs), a framework that augments probabilistic models with declarative constraints as a way to support decisions in an expressive output space while maintaining modularity and tractability of training. The focus will be on discussing training and inference paradigms for Constrained Conditional Models, with examples drawn from natural language understanding tasks such as semantic role learning, information extraction tasks, and transliteration.

If you would like to meet with the speaker, please contact Dietrich Klakow.