Structured Prediction
186 papers with code • 1 benchmarks • 6 datasets
Structured Prediction is an area of machine learning focusing on representations of spaces with combinatorial structure, and algorithms for inference and parameter estimation over these structures. Core methods include both tractable exact approaches like dynamic programming and spanning tree algorithms as well as heuristic techniques such as linear programming relaxations and greedy search.
Libraries
Use these libraries to find Structured Prediction models and implementations
MNIST
ListOps
CFQ
SciREX
WIKIOG
WBM
Most implemented papers
Neural Symbolic Machines: Learning Semantic Parsers on Freebase with Weak Supervision
crazydonkey200/neural-symbolic-machines
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ACL 2017
Harnessing the statistical power of neural networks to perform language understanding and symbolic reasoning is difficult, when it requires executing efficient discrete operations against a large knowledge-base.
Commonly Uncommon: Semantic Sparsity in Situation Recognition
my89/imSitu
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CVPR 2017
Semantic sparsity is a common challenge in structured visual classification problems; when the output space is complex, the vast majority of the possible predictions are rarely, if ever, seen in the training set.
Experiment Segmentation in Scientific Discourse as Clause-level Structured Prediction using Recurrent Neural Networks
We propose a deep learning model for identifying structure within experiment narratives in scientific literature.
A Fast and Scalable Joint Estimator for Integrating Additional Knowledge in Learning Multiple Related Sparse Gaussian Graphical Models
We consider the problem of including additional knowledge in estimating sparse Gaussian graphical models (sGGMs) from aggregated samples, arising often in bioinformatics and neuroimaging applications.
Structured Output Learning with Conditional Generative Flows
Traditional structured prediction models try to learn the conditional likelihood, i. e., p(y|x), to capture the relationship between the structured output y and the input features x.
Learning with Differentiable Perturbed Optimizers
tuero/perturbations-differential-pytorch
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Machine learning pipelines often rely on optimization procedures to make discrete decisions (e. g., sorting, picking closest neighbors, or shortest paths).
Structured Prediction with Partial Labelling through the Infimum Loss
Annotating datasets is one of the main costs in nowadays supervised learning.
Automated Concatenation of Embeddings for Structured Prediction
Pretrained contextualized embeddings are powerful word representations for structured prediction tasks.
A Frustratingly Easy Approach for Entity and Relation Extraction
princeton-nlp/PURE
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NAACL 2021
Our approach essentially builds on two independent encoders and merely uses the entity model to construct the input for the relation model.
DEGREE: A Data-Efficient Generation-Based Event Extraction Model
pluslabnlp/degree
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NAACL 2022
Given a passage and a manually designed prompt, DEGREE learns to summarize the events mentioned in the passage into a natural sentence that follows a predefined pattern.
