Although there is variability in nonnative grammar learning outcomes, the contributions of training paradigm design and memory subsystems are not well understood. To examine this, we presented learners with an artificial grammar that formed words via simple and complex morphophonological rules. Across three experiments, we manipulated training paradigm design and measured subjects' declarative,...

The problem of establishing transferable language structures is considered. The key idea is developing a synergistic approach combining semantic grammar rules with the machine learning mechanisms of grammar rules extraction from parallel text corpora. The predesigned rules are founded on the unified cognitive structures extracted from the systems of grammar categories of the Russian and English...

We aim to replace the long and complicated, pipeline employed to produce probabilistic phrasal bilexica with a theoretically principled, grammar based, approach. To this end, we introduce a learning regime to learn a phrasal grammar equivalent to linear transduction grammars. The stochastic version of this new grammar type also has the property that the set of biterminals constitute a natural p...

Lexicalized Well-Founded Grammar (LWFG) is a recently developed syntacticsemantic grammar formalism for deep language understanding, which balances expressiveness with provable learnability results. The learnability result for LWFGs assumes that the semantic composition constraints are learnable. In this paper, we show what are the properties and principles the semantic representation and gramm...

We introduce a method for learning to predict the following grammar and text of the ongoing translation given a source text. In our approach, predictions are offered aimed at reducing users’ burden on lexical and grammar choices, and improving productivity. The method involves learning syntactic phraseology and translation equivalents. At run-time, the source and its translation prefix are slic...

Grammar induction is the task of learning a grammar from a set of examples. Recently, neural networks have been shown to be powerful learning machines that can identify patterns in streams of data. In this work we investigate their effectiveness in inducing a regular grammar from data, without any assumptions about the grammar. We train a recurrent neural network to distinguish between strings ...

Grammar learning and refinement on the basis of language resources is very appealing in comparison with manual development of formal grammar. But in order to learn a complex grammar a complex resource is needed. Thus the creation of language resources and learning of grammars from them have to be aware of each other. In this paper we define a formal basis for annotation of corpora with respect ...

This paper investigates a gradual on-line learning algorithm for Harmonic Grammar. By adapting existing convergence proofs for perceptrons, we show that for any nonvarying target language, Harmonic-Grammar learners are guaranteed to converge to an appropriate grammar, if they receive complete information about the structure of the learning data. We also prove convergence when the learner incorp...

An avian analogue to human artificial or synthetic grammar learning (Reber, 1967) was developed. Pigeons viewed horizontal strings of three to eight colored letters. These strings either conformed to Reber's artificial grammar or violated it in one or two locations. Pigeons categorized the letter strings as grammatical (left keypeck) or nongrammatical (right keypeck). Overall accuracy of catego...