Model Selection
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Sequential Model Selection for Word Sense Disambiguation
Statistical models of word-sense disam- biguation are often based on a small num- ber of contextual features or on a model that is assumed to characterize the inter- actions among …...
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Model selection (picking, for example, the feature set and the regularization strength) is crucial for building high-accuracy NLP models. In supervised learning, we can es- timate the accuracy of a …...
Boosting for Efficient Model Selection for Syntactic Parsing
AbstractWe present an efficient model selection method using boosting for transition-based constituency parsing. It is designed for exploring a high-dimensional search space, defined by a large set of feature templates, …...
FIESTA: Fast IdEntification of State-of-The-Art models using adaptive bandit algorithms
AbstractWe present FIESTA, a model selection approach that significantly reduces the computational resources required to reliably identify state-of-the-art performance from large collections of candidate models. Despite being known to produce …...
Model Selection for Cross-lingual Transfer
AbstractTransformers that are pre-trained on multilingual corpora, such as, mBERT and XLM-RoBERTa, have achieved impressive cross-lingual transfer capabilities. In the zero-shot transfer setting, only English training data is used, and …...
Evaluation of HTR models without Ground Truth Material
AbstractThe evaluation of Handwritten Text Recognition (HTR) models during their development is straightforward: because HTR is a supervised problem, the usual data split into training, validation, and test data sets …...
Fair Enough: Standardizing Evaluation and Model Selection for Fairness Research in NLP
AbstractModern NLP systems exhibit a range of biases, which a growing literature on model debiasing attempts to correct. However, current progress is hampered by a plurality of definitions of bias, …...