Scott Wen-Tau Yih will give a talk titled "Learning with Integer Linear Programming Inference for Constrained Output". The talk will first demonstrate how constraints can be incorporated into conditional random fields using a novel inference approach based on integer linear programming. This allows CRF models to efficiently support general constraint structures. Experimental results will be provided for semantic role labeling. The second part will compare simple learning plus inference to inference based training, finding the latter is superior when local classifiers are difficult but requires more examples to show differences.

1. Time：11：00AM
Location：New CSIE Building R110
Topic：Learning with Integer Linear Programming Inference for Constrained Output
Speaker： Scott Wen-Tau Yih
Abstract：
In several structured classification problems, explicit and expressive constraints are crucial to
enhancing the accuracy and quality of the predictions. However, it was not clear how this additional
knowledge can be used in various learning frameworks. In this talk, I'll first demonstrate how constraints
can be incorporated in Conditional Random Fields via a novel inference approach based on integer
linear programming. Inference in CRFs and HMMs is usually done using the Viterbi algorithm, an
efficient dynamic programming algorithm. In many cases, general (non-local and non-sequential)
constraints may exist over the output sequence, but cannot be incorporated and exploited in a natural
way by Viterbi. Our inference procedure extends CRF models to naturally and efficiently support general
constraint structures. For sequential constraints, this procedure reduces to simple linear programming
as the inference process. Experimental evidences of our approach will be provided in the context of an
important NLP problem, semantic role labeling.
One interesting phenomenon we observed in the experiments is that a simple learning plus
inference scheme may outperform inference based training approaches when incorporating constraints.
In the second part of my talk, I'll describe how we compared these two learning frameworks by
observing their behaviors in different conditions. Experiments and theoretical justification lead to the
conclusion that using inference based learning is superior when the local classifiers are difficult to learn
but may require many examples before any discernible difference can be observed.
Bio：
Wen-tau Yih is a post-doc researcher in the Machine Learning and Applied Statistics group at
Microsoft Research. He got his Ph.D. at the University of Illinois at Urbana-Champaign in May 2005.
Although his current research focuses mainly on problems related to email applications and anti-spam,
his research interests spread on various problems in Machine Learning and Natural Language
Processing, such as learning and knowledge representation, information extraction, semantic parsing,
and inference and learning for structured output. Wen-tau received both his M.S. and B.S. degrees in
Computer Science from National Taiwan University. More information can be found on his homepage:
http://scottyih.org/