We present a computational (LSTM) model that learns to produce English (3sg and -bare) verb inflection when trained on English child-directed speech (CDS). The model is trained on input containing morphemized verbs and learns to predict the next token (word/morpheme) given a preceding sequence of tokens. The model produces the type of error (-bare for -3s) made by English-learning children while avoiding errors that children do not often make (-3s for -bare). The model also shows the same type of sensitivity to input statistics that has been reported in English-learning children. Finally, we manipulated the length of the sequences the model is trained on and show that this results in the delayed acquisition of -3sg forms that is characteristic of English-learning children with Developmental Language Disorder (DLD). Taken together these results suggest that input-driven learning is a major determinant of the patterns observed in both typical and impaired acquisition of English verb inflection.

Elicited imitation is a widely used method for testing a child's knowledge of a language for scientific or clinical purposes. A child hears an utterance and is asked to repeat what they have heard. While it is assumed that their fluency or speed in doing so is contingent on their linguistic competence, little is known about the cognitive mechanisms and/or representations involved. To explore this, we train an encoder-decoder model, consisting of recurrent neural networks, to encode and reproduce a corpus of child-directed speech and then test its performance on the experimental task of Bannard and Matthews (2008). In that study pre-school children were asked to repeat high- and low-frequency four-word sequences in which the first three words were identical (e.g., sit in your chair and sit in your truck) and the final words and bigrams were closely matched for frequency. We find that like those children our model makes more errors on the initial three words when they are part of a low-frequency than a high-frequency sequence, despite the fact that the words being repeated are identical. We explore why this might be and pinpoint some possible similarities between the model and child language processing.

We present a computational model of the acquisition of German case that is evaluated against empirical data obtained from naturalistic speech. The model substitutes nouns into existing contexts, and proceeds through a number of stages that reflect increasing knowledge on the part of a child, both of the determiner-noun sequences that are legal in German, and of the determiner-noun sequences that are appropriate in specific sentential contexts (cases). The model provides a natural account of gender and case errors, the two most common error types produced by children, and shows the highest error rates in dative contexts and lowest error rates in nominative contexts, as is true of children learning German. However, the model’s error rates in the early stages are considerably higher than those shown by children, suggesting that children possess a fairly sophisticated representation of how lexical contexts assign case from a relatively early age.

In this paper, we examine the success of developmentaldistributional analysis in English, German and Dutch. Weembed the mechanism for distributional analysis within anexisting model of language acquisition (MOSAIC) thatencodes increasingly long utterances, and compare resultsagainst a measure of ‘noun richness’ in child speech. We showthat, cross-linguistically, the mechanism’s success in buildingan early noun class is inversely related to the complexity of thedeterminer and noun gender system, and that merging ofdeterminers gives very similar results across languages. Theseresults suggest that children may represent grammaticalcategories at multiple levels of abstraction that reflect both thelarger category as well as its finer structure.

In this paper we evaluate a mechanism for the learning of wordcategories from distributional information against criteria ofpsychological plausibility. We elaborate on the ideasdeveloped by Redington et al. (1998) by embedding themechanism in an existing model of language acquisition(MOSAIC) and gradually expanding the contexts it has accessto in a developmentally plausible way. In line with child data,the mechanism shows early development of a noun category,and later development of a verb category. It is furthermoreshown that the mechanism can maintain high performance atlower computational overhead by disregarding tokenfrequency information, thus improving the plausibility of themechanism as something that is used by language-learningchildren.

This paper describes an extension to the MOSAIC model which aims to increase MOSAIC’s fit to the cross-linguistic occurrence of Optional Infinitive (OI) errors. While previous versions of MOSAIC have successfully simulated these errors as truncated compound finites with missing modals or auxiliaries, they have tended to underestimate the rate of OI errors in (some) obligatory subject languages. Here, we explore defaulting effects, where the most frequent form of a given verb is substituted for less frequent forms, as an additional source of OI errors. It is shown that defaulting in English tends to result in the production of bare forms that are indistinguishable from the infinitive, while defaulting in Spanish is less pronounced, and tends to result in the production of 3rd person singular forms. Dutch verb forms are dominated by the stem in corpus-wide statistics, and the infinitive in utterance-final position, suggesting defaulting in Dutch may change qualitatively across development. Defaulting is shown to increase MOSAIC’s fit to English and Dutch without affecting its already good fit to Spanish, and provides a potential way of simulating the cross-linguistic pattern of verb-marking errors in children with SLI.