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 41–50 of 639 papers

TitleStatusHype
Instance-Based Learning of Span Representations: A Case Study through Named Entity RecognitionCode1
Interactive Fiction Game Playing as Multi-Paragraph Reading Comprehension with Reinforcement LearningCode1
Active Imitation Learning with Noisy GuidanceCode1
Learning Identifiable Structures Helps Avoid Bias in DNN-based Supervised Causal LearningCode1
Code4Struct: Code Generation for Few-Shot Event Structure PredictionCode1
LP-SparseMAP: Differentiable Relaxed Optimization for Sparse Structured PredictionCode1
DOGE-Train: Discrete Optimization on GPU with End-to-end TrainingCode1
A Frustratingly Easy Approach for Entity and Relation ExtractionCode1
Estimating Gradients for Discrete Random Variables by Sampling without ReplacementCode1
Improving Pretrained Cross-Lingual Language Models via Self-Labeled Word AlignmentCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CVAENegative CLL71.8—Unverified