• Jan 12 of NLP 202 (slides)
• Section 3.1-3.3 of Jeff's thesis gives an very brief overview of loss functions for structured prediction

• Structured Perceptron: Collins 2002 - Discriminative Training Methods for Hidden Markov Models: Theory and Experiments with Perceptron Algorithms
• Structured SVM

Recent papers: https://paperswithcode.com/task/structured-prediction

• Andor et al 2016 - Globally Normalized Transition-Based Neural Networks Talks about the label bias problem.
• Edunov et al 2017 - Classical Structured Prediction Losses for Sequence to Sequence Learning Uses a sample (i.e. beam) size of 16 for the MT experiments. See section 6.5 and Table 2.
• Gómez-Rodríguez et al 2018 - Global Transition-based Non-projective Dependency Parsing
• Ma et al 2019 - Randomized Greedy Search for Structured Prediction: Amortized Inference and Learning
• Collobert 2019 - A Fully Differentiable Beam Search Decoder
• Rush 2020 - Torch-Struct: Deep Structured Prediction Library
• Gao & Gormley 2020 - Training for Gibbs Sampling on Conditional Random Fields with Neural Scoring Factors
• Fonseca & Martins 2020 - Revisiting Higher-Order Dependency Parsers
• Widmoser 2021 - Randomized Deep Structured Prediction for Discourse-Level Processing “Our experiments show that in all cases, deep structured prediction outperforms traditional shallow approaches, structured learning outperforms inference over locally trained models, and generic randomized inference performs competitively to exact inference.”

• Amos et al 2016 - Input Convex Neural Networks