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A Computational Model of General Rule Learning with Unnatural Classes

A Computational Model of General Rule Learning with Unnatural Classes Shira Calamaro (shira.calamaro@yale.edu) Department of Linguistics, 370 Temple Street New Haven, CT 06511 USA classes over unnatural classes may be explained by the inability to generalize over certain classes of segments. This preference is realized in the model through a generalization bias, or preference for general rules. The learnability of some types of unnatural rules is also explained by the model, which can identify the robust patterns present in the data and distinguish between them through the interaction of complexity and competition. Abstract This paper presents the results of a computational model of generalized phonological rule learning (Calamaro and Jarosz, 2012), which is used to model experimental studies on the learning of phonotactic patterns governed by natural and unnatural classes. I focus on two papers with conflicting results on the learnability of natural and unnatural rules. Saffran and Thiessen (2003) find that a phonotactic pattern of positional voicing restrictions governed by a natural class of segments is learned by infants, but a similar pattern governed by an unnatural class is not learned. In contrast, Chambers, Onishi, and Fisher (2003) find that infants can learn a phonotactic pattern governed by an unnatural class of segments. The computational model presented in this paper is able to account for these seemingly conflicting results, explaining both the learnability and unlearnability of rules governed by unnatural classes. Background In their artificial-language experiments on phonological acquisition, Saffran and Thiessen (2003) attempt to find the types of patterns that are learnable by infants and identify the types of pattern that are more difficult to learn. 9-month- old infants were trained on a set of language data exhibiting the specified pattern. They were then tested using the head- turn preference procedure, in which listening times for familiar and novel words were measured. A significant difference in listening times would indicate which patterns had been learned by the infants after a brief training period. In one experiment, they looked at the learning of voicing restrictions in different positions of a syllable. Using two conditions, they restricted the types of consonants that could appear in the onset, the position preceding the vowel, and the coda, the position following the vowel. In one condition, the onset position was restricted to the set of voiceless stops [p,t,k], while the coda was restricted to voiced stops [b,d,g]. For example, words of the form pibtad were permitted, but not *bipdat. In the second condition, the restrictions were reversed, with voiced stops in the onset and voiceless stops in the coda. The sets [p,t,k] and [b,d,g] each form a natural class of stop consonants because they can be distinguished using a single feature, [voice]. The results showed that infants were indeed capable of learning this distinction, with a significant difference in looking times between familiar and novel words. In this experiment, the infants were able to learn a phonotactic pattern governed by a natural class of segments. The next experiment investigated the learning of voicing restrictions of unnatural classes in different prosodic positions. Unlike the previous experiment, in which the sets [p,t,k] and [b,d,g] could be distinguished by the [voice] feature, the sets used in the second experiment cannot be distinguished by any feature, making them unnatural. In one condition, [p,d,k] appeared in the onset while [b,t,g] appeared in the coda. The reverse was true in the second condition, with [b,t,g] appearing in the onset and [p,d,k] occurring in the coda. The experimental results differed, with no significant difference in looking times between the familiar and novel words. In the experiment, the infants Keywords: Linguistics; Phonology; Language Acquisition; Computational Model; Statistical Learning Introduction Many artificial-language experiments have explored the learnability of sound patterns in acquisition and how these may reflect biases in the phonology. The interpretation of experimental results can often be attributed to a number of different theoretical models. In this paper, I explore the results of experimental studies on the learnability of unnatural rules and provide an analysis in a computational model. Experiments in language acquisition have found conflicting results in the learnability of unnatural sound patterns. In one study, Saffran and Thiessen (2003) found that infants were able to learn phonotactic voicing restrictions governed by a natural class of segments, but were unable to learn the same pattern when governed by an unnatural class. In contrast, Chambers, Onishi, and Fisher (2003) have shown that phonotactic patterns governed by unnatural classes may be learnable. In this paper, I present a computational model of generalized rule learning (Calamaro and Jarosz, 2012), which offers an account of the results found by Saffran and Thiessen (2003) and Chambers et al. (2003). This model uses statistical regularities in the input, as well as linguistic filters, to learn phonological alternations. It encodes these patterns as generalized rules over natural classes of segments, which can explain the inability to generalize certain patterns that do not fall into a natural class. The results of these computational experiments will help to further clarify the nature of the results of the acquisition studies. The preference for patterns governed by natural