SOTAVerified

Structured Prediction

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.

Source: Torch-Struct: Deep Structured Prediction Library

Papers

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Showing 26–50 of 639 papers

TitleStatusHype
Promptly Predicting Structures: The Return of InferenceCode0
Lazy-k: Decoding for Constrained Token ClassificationCode0
Unified Low-Resource Sequence Labeling by Sample-Aware Dynamic Sparse FinetuningCode0
SpEL: Structured Prediction for Entity LinkingCode1
A Unified View of Evaluation Metrics for Structured PredictionCode0
An Expression Tree Decoding Strategy for Mathematical Equation GenerationCode2
"A Tale of Two Movements": Identifying and Comparing Perspectives in #BlackLivesMatter and #BlueLivesMatter Movements-related Tweets using Weakly Supervised Graph-based Structured Prediction—0
Contextual Label Projection for Cross-Lingual Structured PredictionCode0
MarkovGen: Structured Prediction for Efficient Text-to-Image Generation—0
Emotion-Conditioned Text Generation through Automatic Prompt Optimization—0
On Regularization and Inference with Label Constraints—0
Training Multimedia Event Extraction With Generated Images and Captions—0
On Certified Generalization in Structured Prediction—0
Partial Inference in Structured Prediction—0
Computing a partition function of a generalized pattern-based energy over a semiring—0
Linear-Time Modeling of Linguistic Structure: An Order-Theoretic Perspective—0
SPEECH: Structured Prediction with Energy-Based Event-Centric HyperspheresCode1
Data-efficient Active Learning for Structured Prediction with Partial Annotation and Self-TrainingCode0
MvP: Multi-view Prompting Improves Aspect Sentiment Tuple PredictionCode1
RxnScribe: A Sequence Generation Model for Reaction Diagram ParsingCode1
Modified Gauss-Newton Algorithms under Noise—0
Mixture of Soft Prompts for Controllable Data GenerationCode0
Sketch In, Sketch Out: Accelerating both Learning and Inference for Structured Prediction with KernelsCode0
PAC Prediction Sets for Large Language Models of CodeCode0
Exact Inference in High-order Structured Prediction—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CVAENegative CLL71.8—Unverified