Difference between revisions of "Daume and Marcu 2005 Learning as Search Optimization: Approximate Large Margin Methods for Structured Prediction"
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| − | + | This is an article about a [[Category::paper]]. | |
| − | [http://hal3.name/docs/daume05laso.pdf | + | == Citation and Link == |
| + | Hal Daumé III and Daniel Marcu. 2005. Learning as search optimization: approximate large margin methods for structured prediction. In ''Proceedings of the 22nd international conference on Machine learning'' (ICML '05). ACM, New York, NY, USA, 169-176. | ||
| + | [http://hal3.name/docs/daume05laso.pdf Pdf version] | ||
| − | + | == Summary == | |
| − | 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 for structured prediction. The algorithm is basically [[UsesMethod::SEARN]] but analyzed differently (and also ~24 pages shorter). |
| − | LaSO attempts to combine | + | LaSO attempts to combine learning the model with the search that occurs during decoding for structured prediction. 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 generic search (decoding) algorithm is shown below: | ||
| Line 15: | Line 17: | ||
[[File:LaSO Generic Search.png]] | [[File:LaSO Generic Search.png]] | ||
| − | 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 ''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. |
| + | |||
| + | In LaSO ''enqueue'' ranks nodes according to a function ''g'' which is a linear in the set of features. The features can depend on the input ''x'' and the path to the current current node ''n'': | ||
<math>g=\mathbf{\omega}^\top \mathbf{\Phi}(x,n)</math> | <math>g=\mathbf{\omega}^\top \mathbf{\Phi}(x,n)</math> | ||
| − | LaSO | + | LaSO learns the feature weights from the training examples. The learning algorithm is shown below: |
[[File:LaSO Algorithm.png]] | [[File:LaSO Algorithm.png]] | ||
| Line 25: | Line 29: | ||
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. | 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 | + | If the search makes a mistake, the weights are updated with the function ''update''. The two update methods they propose in the paper are the [[UsesMethod::Voted Perceptron]] and a variant of the approximate large margin update (ALMA). |
| + | |||
| + | * '''Perceptron update rule:''' | ||
| + | |||
| + | <math>\mathbf{\omega} \leftarrow \mathbf{\omega} + \Delta</math> | ||
[[File:LaSO Perceptron Update.png]] | [[File:LaSO Perceptron Update.png]] | ||
| − | === Experimental Result == | + | * '''ALMA update rule:''' |
| + | |||
| + | <math>\mathbf{\omega} \leftarrow \mathbf{\omega} + \wp(\mathbf{\omega} + Ck^{-1/2} \wp(\Delta))</math> | ||
| + | |||
| + | where | ||
| + | |||
| + | <math>\wp(\mathbf{u}) = \mathbf{u} / \max \{1,\lVert \mathbf{u} \rVert_{2} \}</math> | ||
| + | |||
| + | and k starts at 1 and is incremented at each update. | ||
| + | |||
| + | == Experimental Result == | ||
| + | |||
| + | They performed experiments on two tasks: syntactic chunking and joint tagging and chunking. They used the CoNLL 2000 dataset. The results are shown below. LASOP denotes LaSO trained with the perceptron, and LASOA denotes LaSO trained with ALMA. The subscript number denotes the beam size. | ||
| + | |||
| + | [[File:LaSO Table 1.png]] | ||
| + | |||
| + | [[File:LaSO Table 2.png]] | ||
| − | + | == Related Papers and Pages == | |
| − | + | LaSO attempts to do the same thing as [[RelatedPaper::Daume et al, ML 2009]], but accomplishes it in a slightly different framework. | |
Latest revision as of 02:08, 11 October 2011
This is an article about a paper.
Citation and Link
Hal Daumé III and Daniel Marcu. 2005. Learning as search optimization: approximate large margin methods for structured prediction. In Proceedings of the 22nd international conference on Machine learning (ICML '05). ACM, New York, NY, USA, 169-176. Pdf version
Summary
The authors present the Learning as Search Optimization (LaSO) framework for structured prediction. The algorithm is basically SEARN but analyzed differently (and also ~24 pages shorter).
LaSO attempts to combine learning the model with the search that occurs during decoding for structured prediction. 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.
In LaSO enqueue ranks nodes according to a function g which is a linear in the set of features. The features can depend on the input x and the path to the current current node n:
LaSO learns the feature weights 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 update methods they propose in the paper are the Voted Perceptron and a variant of the approximate large margin update (ALMA).
- Perceptron update rule:
- ALMA update rule:
where
and k starts at 1 and is incremented at each update.
Experimental Result
They performed experiments on two tasks: syntactic chunking and joint tagging and chunking. They used the CoNLL 2000 dataset. The results are shown below. LASOP denotes LaSO trained with the perceptron, and LASOA denotes LaSO trained with ALMA. The subscript number denotes the beam size.
Related Papers and Pages
LaSO attempts to do the same thing as Daume et al, ML 2009, but accomplishes it in a slightly different framework.
