One longstanding impediment to progress in understanding the neural basis of language is the development of model systems that retain language-relevant cognitive behaviors yet permit invasive cellular neuroscience methods. Recent experiments in songbirds suggest that this group may be developed into a powerful animal model, particularly for components of grammatical processing. It remains unkno...

European starlings have the ability to learn to classify complex grammatical patterns that follow a context-free grammar (CFG) composed of song motifs (Gentner, et al., 2006). Cotton-top tamarins cannot classify CFG patterns, when exposed to patterns composed of human speech (Fitch & Hauser, 2004). Beyond differences in species-specific cognitive ability, methodological differences might accoun...

The robustness of probabilistic parsing generally comes at the expense of grammaticality judgment – the grammaticality of the most probable output parse remaining unknown. Parsers, such as the Stanford or the Reranking ones, can not discriminate between grammatical and ungrammatical probable parses, whether their surface realisations are themselves grammatical or not. In this paper we show that...

We report the case of a brain-damaged subject R.C. who is more impaired at producing grammatical forms of words and pseudo-words used as verbs (he judges, he wugs) than of the same words used as nouns (the judges, the wugs). This pattern of performance constitutes the first clear demonstration that grammatical knowledge about verbs can be selectively impaired following brain damage. A compariso...

Automatic generation of models from a set of positive and negative samples and a-priori knowledge (if available) is a crucial issue for pattern recognition applications. Grammatical inference can play an important role in this issue since it is one of the methodologies that can be used to generate the set of model classes, where each class consists on the rules to generate the models. In this p...

Language learners must place unfamiliar words into categories, often with few explicit indicators about when and how that word can be used grammatically. Reeder, Newport, and Aslin (2013) showed that college students can learn grammatical form classes from an artificial language by relying solely on distributional information (i.e., contextual cues in the input). Here, 2 experiments revealed th...

There has been an increased interest in data generation approaches to grammatical error correction (GEC) using pseudo data. However, these suffer from several issues that make them inconvenient for real-world deployment including a demand large amounts of training On the other hand, some errors based on rules may not necessarily require amount if GEC models can realize generalization. This stud...

Artificial grammar learning (AGL) is a form of nondeclarative memory that involves the nonconscious acquisition of abstract rules. While data from amnesic patients indicate that AGL does not depend on the medial temporal lobe, the neural basis of this type of memory is unknown and was therefore examined using event-related fMRI. Prior to scanning, participants studied letter strings constructed...

In this paper we present an approach to train subatom embeddings for verbs. For each verb we learn not just one embedding, but several. One for the verb itself and embeddings for each grammatical role of this verb. For example, for the verb ‘to give’ we learn four embeddings: one for the lemma ‘give’, one for the subject, one for the direct object and one for the indirect object of it. We are e...