Which factors determine the choice of a plural allomorph for a new singular form? Are regular mappings stored differently from irregular mappings? Do native speakers only rely on analogical mappings to inflect novel word forms or do they use rules? To answer these questions we used data from Maltese, a language with a split morphology, which has a rich and variable set of concatenative and non-concatenative plural patterns.We conducted a production experiment, in which we investigated the mapping of a singular onto a plural. We show that this is driven by an interplay between the similarity of novel singular forms with existing singular words and their corresponding plural forms. Moreover, knowledge of the frequency of the plural patterns in the mental lexicon serves as a basis for generalization to novel words. Our results support an analogical model of morphological processing. We do not find evidence that native speakers use default rules.

Word-based models of morphology propose that complex words are stored without reference to morphemes. One of the questions that arises is whether information about word forms alone is enough to determine a noun's number from its form. We take up this question by modelling the classification and production of the Maltese noun plural system, using models that do not assume morphemic representations. We use the Tilburg Memory-Based Learner, a computational implementation of exemplar theory and the Naive Discriminative Learner, an implementation of Word and Paradigm, for classification. Both models classify Maltese nouns well. In their current implementations, TiMBL and NDL cannot concatenate sequences of phones that result in word forms. We used two neural networks architectures (LSTM and GRU) to model the production of plurals. We conclude that the Maltese noun plural system can be modelled on the basis of whole words without morphemes, supporting word-based models of morphology.

This study challenges a computational implementation of Word and Paradigm Morphology with the task of modeling the semi-productive noun system of Maltese, which combines a dozen concatenative plural patterns with eleven non-concatenative plural patterns. We show that our model, trained on 6,511 word forms, generates accurate predictions about what meanings listeners understand and what forms speakers produce. Furthermore, measures derived from the model are predictive for Maltese reaction times. Although mathematically very simple, the linear mappings between form and meaning posited by our model are powerful enough to capture the complexity and productivity of the Maltese noun system.

Comprehending and producing words is a natural process for human speakers. In linguistic theory, investigating this process formally and computationally is often done by focusing on forms only. By moving beyond the world of forms, we show in this study that the Discriminative Lexicon (DL) model operating with word comprehension as a mapping of form onto meaning and word production as a mapping of meaning onto form generates accurate predictions about what meanings listeners understand and what forms speakers produce. Furthermore, we show that measures derived from the computational model are predictive for human reaction times. Although mathematically very simple, the linear mappings between form and meaning posited by our model are powerful enough to capture the complexity and productivity of a Semitic language with a complex hybrid morphological system. 1

Grammatical knowledge of native speakers has often been investigated in so-called wug tests, in which participants have to inflect pseudo-word forms (wugs). Typically it has been argued that in inflecting these pseudo-words, speakers apply their knowledge of word formation processes. However, it remains unclear what exactly this knowledge is and how it is learned.According to one theory, the knowledge is best characterized as abstractions and rules that specify how units can be combined. Another theory maintains that it is best characterized by analogy. In both cases the knowledge is learned by association based on positive evidence alone.In this paper, we model the classification of pseudo-words to Maltese plural classes on the basis of phonetic input using a shallow neural network trained with an error-driven learning algorithm. We demonstrate that the classification patterns mirror those of Maltese native speakers in a wug test.Our results indicate that speakers rely on gradient knowledge of a relation between the phonetics of whole words and plural classes, which is learned in an error-driven way.