Lafferty et al 2001 - Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data Introduced the label bias problem, which is present in Maximum-Entropy Markov Models (MEMMs) but not Conditional Random Fields (CRFs). This drawback of MEMMs was one of the main reasons for inventing CRFs

Murray & Chiang 2018 - Correcting Length Bias in Neural Machine Translation Argues that the beam search problem in NMT occurs because of the label bias problem (see section 2)

Andor et al 2016 - Globally Normalized Transition-Based Neural Networks Proves that global models can be strictly more expressive than local models.

Goyal et al 2019 - An Empirical Investigation of Global and Local Normalization for Recurrent Neural Sequence Models Using a Continuous Relaxation to Beam Search

Structured Prediction