Structural Learning with Amortized Inference
Authors: Kai-Wei Chang, Shyam Upadhyay, Gourab Kundu, Dan Roth
AAAI 2015 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We show the efficacy of our method by training a structured SVM using dual coordinate descent for an entity-relation extraction task. Our experiments aim to show the following properties: (1) the amortization techniques significantly reduce the number of inference solver calls, and hence the training time is reduced; (2) with an adaptation on ϵ, the model converges to the same objective function value; (3) with the help of pre-cached examples, we achieve higher amortization ratio and further speed up the training process. Table 1: Ratio of inference engine calls, inference time, inference speedup, final negative dual objective function value ( D(α) in Eq. (6)) and the test performance of each method. |
| Researcher Affiliation | Academia | Kai-Wei Chang, Shyam Upadhyay, Gourab Kundu, and Dan Roth Department of Computer Science University of Illinois at Urbana-Champaign {kchang10,upadhya3,kundu2,danr}@illinois.edu |
| Pseudocode | Yes | Algorithm 1 A Dual Coordinate Descent Method with Amortized Inference |
| Open Source Code | No | The paper states: 'We implemented our algorithms based on a publicly available Structured SVM package4 in JAVA' and provides a footnote link to 'http://cogcomp.cs.illinois.edu/page/software view/JLIS'. This link points to a general software page for a package they used, not specifically the source code for the methodology described in this paper. |
| Open Datasets | Yes | We use the annotated corpus from Roth and Yih (2007), which consists of 5,925 sentences. ... We use a dataset, scene3, with 6 labels and 1,211 instances to demonstrate the performance of our method. ... 3Available at http://www.csie.ntu.edu.tw/ cjlin/libsvmtools/ datasets |
| Dataset Splits | Yes | performing a 5-fold cross validation for picking the best regularization parameter C for structured SVM on the entity-relation recognition task. |
| Hardware Specification | Yes | We implemented our algorithms based on a publicly available Structured SVM package4 in JAVA and conducted experiments on a machine with Xeon E5-2440 processors. |
| Software Dependencies | No | The paper mentions 'JAVA' and 'Gurobi' but does not provide specific version numbers for these or any other software dependencies. |
| Experiment Setup | Yes | Unless otherwise stated, we show the performance of training Structured SVM model with C = 0.1 with stopping condition δ = 0.1. For Perceptron, we used an averaged Perceptron implementation in JAVA and set the max number of iterations to 100. ... We choose C from {0.01, 0.5 0.1, 0.5, 1}, running 5 folds for each, for a total of 25 runs. δ is set to 0.1 for all folds. |