We present a family of priors over probabilistic grammar weights, called the shared logistic normal distribution. This family extends the partitioned logistic normal distribution, enabling factored covariance between the probabilities of different derivation events in the probabilistic grammar, providing a new way to encode prior knowledge about an unknown grammar. We describe a variational EM ...

Grammatical Inference deals with the problem of learning structural models, such as grammars, from different sort of data patterns, such as artificial languages, natural languages, biosequences, speech and so on. This article describes a new grammatical inference tool, Grammar-based Classifier System (GCS) dedicated to learn grammar from data. GCS is a new model of Learning Classifier Systems i...

In this paper, we investigate one-shot learning in the context of artificial categories with abstract structural characteristics. We propose a grammar-based approach to structure representation, which allows us to treat visual category learning as an instance of grammar induction. We provide a model of one-shot learning as Bayesian inference over hypotheses, where grammars are hypotheses and le...

An unsupervised incremental algorithm for grammar inference and its application to domain-specific language development are described. Grammatical inference is the process of learning a grammar from the set of positive and optionally negative sentences. Learning general context-free grammars is still considered a hard problem in machine learning and is not completely solved yet. The main contri...

This study aimed to investigate the relative effectiveness of consciousness-raising tasks and structure-based production tasks in comparison with the traditional teaching in learning comparative and superlative forms, following a task-based approach to teaching English grammar. To this end, from among 82 female elementary-level high school students having taken a Solutions Placement Test (2010)...

The effect of error feedback on the accuracy of output task types such as editing task, text reconstruction task, picture cued writing task, and dictogloss task, has not been clearly explored. Following arguments concerning that the combination of both corrective feedback and output makes it difficult to determine whether their effects were in combination or alone, the purpose of the present st...

Previous studies have suggested that both automatic and intentional processes contribute to the learning of grammar and fragment knowledge in artificial grammar learning (AGL) tasks. To explore the relative contribution of automatic and intentional processes to knowledge gained in AGL, we utilized dual-task methodology to dissociate automatic and intentional grammar- and fragment-based knowledg...

25 Humans and nonhuman primates can learn about the organization of stimuli in the environment 26 using implicit sequential pattern learning capabilities. However, most previous artificial grammar 27 learning studies with nonhuman primates have involved relatively simple grammars and short input 28 sequences. The goal in the current experiments was to assess the learning capabilities of monkeys...

This study aimed to investigate the relative effectiveness of consciousness-raising tasks and structure-based production tasks in comparison with the traditional teaching in learning comparative and superlative forms, following a task-based approach to teaching English grammar. To this end, from among 82 female elementary-level high school students having taken a Solutions Placement Test (2010)...

This study attempted to evaluate and compare language learning apps and the related traditional books on the same subject. The apps included Murphy’s English Grammar and Cambridge Discovery Readers and the traditional materials were English Grammar in Use and Developing Reading Skills. The study, thus, aimed to do a comparative analysis between traditional ELT materials and the digital versions...