Difference between revisions of "Daume and Marcu 2005 Learning as Search Optimization: Approximate Large Margin Methods for Structured Prediction"
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[http://hal3.name/docs/daume05laso.pdf Learning as Search Optimization: Approximate Large Margin Methods for Structured Prediction] An alternative formal analysis of Searn. | [http://hal3.name/docs/daume05laso.pdf Learning as Search Optimization: Approximate Large Margin Methods for Structured Prediction] An alternative formal analysis of Searn. | ||
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The authors present the Learning as Search Optimization (LaSO) framework. The algorithm is basically SEARN but analyzed differently (and also ~24 pages shorter). | The authors present the Learning as Search Optimization (LaSO) framework. The algorithm is basically SEARN but analyzed differently (and also ~24 pages shorter). | ||
| − | + | LaSO attempts to combine the learning of the model with the search that occurs during decoding. Instead of learning the model and then doing a search during decoding, LaSO attempts to directly learn to search. | |
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=== Method === | === Method === | ||
Revision as of 04:39, 1 October 2011
Citation and Link
Learning as Search Optimization: Approximate Large Margin Methods for Structured Prediction An alternative formal analysis of Searn.
Summary
The authors present the Learning as Search Optimization (LaSO) framework. The algorithm is basically SEARN but analyzed differently (and also ~24 pages shorter).
LaSO attempts to combine the learning of the model with the search that occurs during decoding. Instead of learning the model and then doing a search during decoding, LaSO attempts to directly learn to search.
Method
The generic search (decoding) algorithm is shown below:
The enqueue function puts the nodes onto the queue in some order. Depending on the order that the enqueue function puts nodes on the queue, you can get depth-first, breadth-first, beam, heuristic, A*, etc search algorithms from standard AI textbooks. The thing that is different about LaSO is the function enqueue. It ranks the nodes according to g which is a linear function of features. The features can depend on the input x and the path to the current current node n:
LaSO works by learning the enqueue function from the training examples. The learning algorithm is shown below:
Nodes for which there exists a path to the optimal goal are called "y-good" nodes. The siblings function denotes the set of nodes at the same depth as current node that can reach the goal (i.e. are y-good). The algorithm may have to backtrack to find them if they are not currently in the queue.
If the search makes a mistake, the weights are updated with the function update. The two functions they propose in the paper are the perceptron update (shown below) and a variant of the approximate large margin update (ALMA).
Experimental Result
Related Papers
In progress by User:Jmflanig
