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 141–150 of 639 papers

TitleStatusHype
Reciprocal Supervised Learning Improves Neural Machine TranslationCode0
Supertagging the Long Tail with Tree-Structured Decoding of Complex CategoriesCode0
A Graph-Based Framework for Structured Prediction Tasks in Sanskrit—0
Learning with Differentiable Pertubed Optimizers—0
Structured Prediction for Joint Class Cardinality and Entity Property Inference in Model-Complete Text Comprehension—0
Energy-based Neural Modelling for Large-Scale Multiple Domain Dialogue State Tracking—0
An Empirical Exploration of Local Ordering Pre-training for Structured Prediction—0
Keep it Surprisingly Simple: A Simple First Order Graph Based Parsing Model for Joint Morphosyntactic Parsing in Sanskrit—0
A Frustratingly Easy Approach for Entity and Relation ExtractionCode1
Document-level Event Extraction with Efficient End-to-end Learning of Cross-event Dependencies—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CVAENegative CLL71.8—Unverified