695 research outputs found

    Complementing quantitative typology with behavioral approaches: Evidence for typological universals

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    Two main classes of theory have been advanced to explain correlations between linguistic features like those observed by Greenberg (1963). arbitrary constraint theories argue that certain sets of features patterm together because they have a single underlying cause in the innate language faculty (e.g., the Principles and Parameters program; see Chomsky & Lasnik 1993). functional theories argue that languages are less likely to have certain combinations of properties because, although possible in principle, they are harder to learn or to process, or less suitable for efficient communication (Hockett 1960, Bates & MacWhinney 1989, Hawkins 2004, Dryer 2007, Christiansen & Chater 2008; for further discussion see Hawkins 2007 and Jaeger & Tily 2011). The failure of Dunn, Greenhill, Levinson & Gray (2011) to find systematic feature correlations using their novel computational phylogenetic methods calls into question both of these classes of theory.Alfred P. Sloan Foundation. Fellowshi

    Typological gaps in iambic nonfinality correlate with learning difficulty

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    This paper discusses gaps in stress typology that are unexpected from the perspective of a foot-based theory and shows that the patterns pose difficulties for a computationally implemented learning algorithm. The unattested patterns result from combining theoretical elements whose effects are generally well-attested, including iambic footing, nonfinality, word edge alignment and a foot binarity requirement. The patterns can be found amongst the 124 target stress systems constructed by Tesar and Smolensky (2000) as a test of their approach to hidden structure learning. A learner with a Maximum Entropy grammar that uses a form of Expectation Maximization to deal with hidden structure was found to often fail on these unattested languages

    Predictive structure or paradigm size? Investigating the effects of i-complexity and e-complexity on the learnability of morphological systems

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    Research on cross-linguistic differences in morphological paradigms reveals a wide range of variation on many dimensions, including the number of categories expressed, the number of unique forms, and the number of inflectional classes. However, in an influential paper, Ackerman and Malouf (2013) argue that there is one dimension on which languages do not differ widely: in predictive structure. Predictive structure in a paradigm describes the extent to which forms predict each other, called i-complexity. Ackerman and Malouf (2013) show that although languages differ according to measure of surface paradigm complexity, called e-complexity, they tend to have low i-complexity. They conclude that morphological paradigms have evolved under a pressure for low i-complexity. Here, we evaluate the hypothesis that language learners are more sensitive to i-complexity than e-complexity by testing how well paradigms which differ on only these dimensions are learned. This could result in the typological findings Ackerman and Malouf (2013) report if even paradigms with very high e-complexity are relatively easy to learn, so long as they have low i-complexity. First, we summarize a recent work by Johnson et al. (2020) suggesting that both neural networks and human learners may actually be more sensitive to e-complexity than i-complexity. Then we build on this work, reporting a series of experiments which confirm that, indeed, across a range of paradigms that vary in either e- or icomplexity, neural networks (LSTMs) are sensitive to both, but show a larger effect of e-complexity (and other measures associated with size and diversity of forms). In human learners, we fail to find any effect of i-complexity on learning at all. Finally, we analyse a large number of randomly generated paradigms and show that e- and i-complexity are negatively correlated: paradigms with high e-complexity necessarily show low i-complexity. We discuss what these findings might mean for Ackerman and Malouf’s hypothesis, as well as the role of ease of learning versus generalization to novel forms in the evolution of paradigms

    Factors 2 and 3: Towards a principled approach

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    This paper seeks to make progress in our understanding of the non-UG components of Chomsky's (2005) Three Factors model. In relation to the input (Factor 2), I argue for the need to formulate a suitably precise hypothesis about which aspects of the input will qualify as 'intake' and, hence, serve as the basis for grammar construction. In relation to Factor 3, I highlight a specific cognitive bias that appears well motivated outside of language, while also having wide-ranging consequences for our understanding of how I-language grammars are constructed, and why they should have the crosslinguistically comparable form that generativists have always argued human languages have. This is Maximise Minimal Means (MMM). I demonstrate how its incorporation into our model of grammar acquisition facilitates understanding of diverse facts about natural language typology, acquisition, both in "stable" and "unstable" contexts, and also the ways in which linguistic systems may change over time.Aquest treball pretén fer progressos en la comprensió dels components que no són UG del model de tres factors de Chomsky (2005). En relació amb l'entrada (factor 2), argumento la necessitat de formular una hipòtesi adequada i precisa sobre quins aspectes de l'entrada es qualificaran com a "ingesta" i, per tant, seran la base de la construcció gramatical. En relació amb el factor 3, destaco un biaix cognitiu específic que apareix força motivat fora del llenguatge, alhora que té àmplies conseqüències per a la nostra comprensió de com es construeixen les gramàtiques del llenguatge I, i per què haurien de tenir la forma interlingüísticament comparable als generativistes. Es tracta de maximitzar els mitjans mínims (MMM). Demostro que la seva incorporació al nostre model d'adquisició gramatical facilita la comprensió de fets diversos sobre tipologia de llenguatge natural, adquisició, tant en contextos "estables" com "inestables", i també de les maneres de canviar els sistemes lingüístics amb el pas del temps

    Learning Local Phonological Processes

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    We present a learning algorithm for local phonological processes that relies on a restriction on the expressive power needed to compute phonological patterns that apply locally. Representing phonological processes as a functional mapping from an input to output form (an assumption compatible with either the SPE or OT formalism), the learner assumes the target process can be described with the functional counterpart to the Strictly Local (McNaughton and Papert 1971, Rogers and Pullum 2011) formal languages. Given a data set of input-output string pairs, the learner applies the two-stage grammatical induction procedure of 1) constructing a prefix tree representation of the input and 2) generalizing the pattern to words not found in the data set by merging states (Garcia and Vidal 1990, Oncina et al. 1993, Heinz 2007, 2009, de la Higuera 2010). The learner’s criterion for state merging enforces a locality requirement on the kind of function it can converge to and thereby directly reflects its own hypothesis space. We demonstrate with the example of German final devoicing, using a corpus of string pairs derived from the CELEX2 lemma corpus. The implications of our results include a proposal for how humans generalize to learn phonological patterns and a consequent explanation for why local phonological patterns have this property

    Predictive structure and the learnability of inflectional paradigms: investigating whether low i-complexity benefits human learners and neural networks

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    Research on cross-linguistic differences in morphological paradigms reveals a wide range of variation on many dimensions, including the number of categories expressed, the number of unique forms, and the number of inflectional classes. This typological variation is surprising within the approach that languages evolve to maximise learnability (e.g., Christiansen and Chater 2008; Deacon 1997; Kirby 2002). Ackerman and Malouf (2013) argue that there is one dimension on which languages do not differ widely: in predictive structure. Predictive structure in a paradigm describes the extent to which forms predict each other, sometimes called i-complexity. Ackerman and Malouf (2013) show that although languages differ ac- cording to surface paradigm complexity measures, called e-complexity, they tend to have low i-complexity. While it has been suggested that i-complexity affects the task of producing unknown forms (the Paradigm Cell Filling Problem, Ackerman, James P. Blevins, et al. 2009; Ackerman and Malouf 2015), its effect on the learnability of morphological paradigms has not been tested. In a series of artificial language learning tasks both with human learners and LSTM neural networks, I evaluate the hypothesis that learners are sensitive to i-complexity by testing how well paradigms which differ on this dimension are learned. In Part 1, I test whether learners are sensitive to i-complexity when learning inflected forms in a miniature language. In Part 2, I compare the effect of i-complexity on learning with that of e-complexity and assess the relationship between these two measures, using randomly con- structed paradigms. In Part 3, I test the effect of i-complexity on learning and generalisation tasks, manipulating the presence of extra-morphological cues for class membership. Results show weak evidence for an effect of i-complexity on learning, with evidence for greater effects of e-complexity in both human and neural network learners. A strong nega- tive correlation was found between i-complexity and e-complexity, suggesting that paradigms with higher surface paradigm complexity tend to have more predictive structure, as mea- sured by i-complexity. There is no evidence for an interaction between i-complexity and extra-morphological cues on learning and generalisation. This suggests that semantic or phonological cues for class membership, which are common in natural languages, do not enhance the effect of i-complexity on learning and generalisation. Finally, i-complexity was found to affect generalisation in both human and neural network learners, suggesting that i-complexity could, in principle, shape languages through the process of generalisation to unknown forms. I discuss the difference in the effects of i-complexity on learning and generalisation, the similarities between the effect of i-complexity in human learners and neural networks, and cases the two types of learner differed. Finally, I discuss the role that i-complexity is likely to have in language change based on the results

    Constraints on Language Learning : behavioral and neurocognitive studies with adults and children

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    This thesis will contribute to a body of experimental work addressing the question of whether language learning plays a role in certain fundamental design properties of natural languages. Methodologically, this thesis seeks to extend the artificial language learning paradigm, investigating whether learners are sensitive to the constraints embodied by key properties of languages. For example, we will explore whether communicative pressure influences the final outcome of language learning, namely how the structures that are acquired by individuals are transmitted to downstream generations. We will also explore how basic language learning constraints operate in different age groups and, importantly, cross-linguistically. Next to the behavioral experiments focusing on learning and its outcomes, we will look at preliminary electrophysiological correlates of basic compositional processing in the early stages of learning a miniature artificial language using electroencephalography (EEG). In this general introduction I will briefly discuss some of the relevant concepts and methods which will be used in three studies that constitute this thesis

    “Natural” stress patterns and dependencies between edge alignment and quantity sensitivity

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    We conducted an artificial language learning experiment to study learning asymmetries that might reveal latent preferences relating to, and any dependencies between, the edge alignment and quantity sensitivity (QS) parameters in stress patterning. We used a poverty of the stimulus approach to teach American English speakers an unbounded QS stress rule (stress a single CV: syllable) and either a left- or right-aligning QI rule if only light syllables were present. Forms with two CV: syllables were withheld in the learning phase and added in the test phase, forcing participants to choose between left- and right-aligning options for the QS rule. Participants learned the left- and right-edge QI rules equally well, and also the basic QS rule. Response patterns for words with two CV: syllables suggest biases favoring a left-aligning QS rule with a left-edge QI default. Our results also suggest that a left-aligning QS pattern with a rightedge QI default was least favored. We argue that stress patterns shown to be preferred based on evidence from ease-of-learning and participants’ untrained generalizations can be considered more natural than less favored opposing patterns. We suggest that cognitive biases revealed by artificial stress learning studies may have contributed to shaping stress typology.publishedVersio
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