nlp:label_bias_problem
Table of Contents
Label Bias Problem
Papers
- 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.
Related Pages
nlp/label_bias_problem.txt · Last modified: 2023/06/15 07:36 by 127.0.0.1