Addressing acquisition from language change: A modeling perspective

info:eu-repo/semantics/publishe

Immature Representation or Immature Deployment? Modeling Child Pronoun Resolution

Children acquiring Spanish interpret subject pronouns differently from adults, initially relying on pragmatic cues instead of morphosyntactic cues that are more statistically reliable. Following Gagliardi et al. (2017), we use Bayesian cognitive modeling to explore the sources of this non-adult-like behavior, investigating whether it is more likely due to (i) noise in children’s representation of the probability that some cues favor certain antecedents, or (ii) noise in children’s deployment of otherwise adult-like probabilities. Results favor noisy deployment as the source of children’s non-adult-like pronoun resolution

Learning Constraints on Wh-Dependencies by Learning How to Efficiently Represent Wh-Dependencies: A Developmental Modeling Investigation With Fragment Grammars

It’s hotly contested how children learn constraints on wh-dependencies, called syntactic islands. When learning this knowledge, a prerequisite is knowing how to represent wh-dependencies so that constraints can be hypothesized over those representations. Previous work has explained disparate sets of syntactic island constraints by assuming different wh-dependency representations, without a unified dependency representation capturing all these constraints. We implement a modeled learner who learns a Fragment Grammar (FG) representation of wh-dependencies–a representation comprised of potentially different-sized fragments that combine to form full dependencies–that best accounts for the input while being as compact as possible. This efficient wh-dependency representation can then be used to generate any wh-dependency’s probability, and so predict acceptability patterns for stimuli sets that reveal syntactic island knowledge. We find that the identified FG can generate the attested acceptability judgment patterns for all syntactic islands previously investigated, highlighting how implicit knowledge of wh-dependency constraints can emerge from trying to learn to efficiently represent wh-dependencies more generally. We additionally compare the FG representation’s performance against baselines inspired by previous proposals, finding that one baseline also yields equivalent performance. We discuss how this baseline is similar to and different from the FG representation

Every Quantifier Isn\u27t the Same: Informativity Matters for Ambiguity Resolution in Quantifier-Negation Sentences

Empirical work on quantifier-not sentences has focused primarily on universal quantifiers, exploring the ambiguity that arises when logical operators interact (e.g., Everyone didn’t go could mean No one went or Not all went). In their Rational Speech Act model of this ambiguity resolution, Savinelli et al.(2017) demonstrate that pragmatic factors (such as model priors over the likely world states) stand to explain divergent behavior between children and adults. We extend this work to a broader empirical base, exploring the model\u27s predictions for sentences with a wider range of quantifiers (some, no); we then test those predictions against behavioral data collected in a series of experiments. We find that a straightforward extension of the Savinelli et al. model captures the range of quantifier-not behavior we gather, thereby providing strong support for this cognitive model of probabilistic ambiguity resolution. In particular, the model explains interpretation preferences on the basis of informativity and prior beliefs over world states, such that interpretations that are more informative are preferred

Corpus evidence for the role of world knowledge in ambiguity reduction: Using high positive expectations to inform quantifier scope

Every-negation utterances (e.g., Every vote doesn’t count) are ambiguous between a surface scope interpretation (e.g., No vote counts) and an inverse scope interpretation (e.g., Not all votes count). Investigations into the interpretation of these utterances have found variation: child and adult interpretations diverge (e.g., Musolino 1999) and adult interpretations of specific constructions show considerable disagreement (Carden 1973, Heringer 1970, Attali et al. 2021). Can we concretely identify factors to explain some of this variation and predict tendencies in individual interpretations? Here we show that a type of expectation about the world (which we call a high positive expectation), which can surface in the linguistic contexts of every-negation utterances, predicts experimental preferences for the inverse scope interpretation of different every-negation utterances. These findings suggest that (1) world knowledge, as set up in a linguistic context, helps to effectively reduce the ambiguity of potentiallyambiguous utterances for listeners, and (2) given that high positive expectations are a kind of affirmative context, negation use is felicitous in affirmative contexts (e.g., Wason 1961)

Necessary Bias in Natural Language Learning

This dissertation investigates the mechanism of language acquisition given the boundary conditions provided by linguistic representation and the time course of acquisition. Exploration of the mechanism is vital once we consider the complexity of the system to be learned and the non-transparent relationship between the observable data and the underlying system. It is not enough to restrict the potential systems the learner could acquire, which can be done by defining a finite set of parameters the learner must set. Even supposing that the system is defined by n binary parameters, we must still explain how the learner converges on the correct system(s) out of the possible 2^n systems, using data that is often highly ambiguous and exception-filled. The main discovery from the case studies presented here is that learners can in fact succeed provided they are biased to only use a subset of the available input that is perceived as a cleaner representation of the underlying system. The case studies are embedded in a framework that conceptualizes language learning as three separable components, assuming that learning is the process of selecting the best-fit option given the available data. These components are (1) a defined hypothesis space, (2) a definition of the data used for learning (data intake), and (3) an algorithm that updates the learner's belief in the available hypotheses, based on data intake. One benefit of this framework is that components can be investigated individually. Moreover, defining the learning components in this somewhat abstract manner allows us to apply the framework to a range of language learning problems and linguistics domains. In addition, we can combine discrete linguistic representations with probabilistic methods and so account for the gradualness and variation in learning that human children display. The tool of exploration for these case studies is computational modeling, which proves itself very useful in addressing the feasibility, sufficiency, and necessity of data intake filtering since these questions would be very difficult to address with traditional experimental techniques. In addition, the results of computational modeling can generate predictions that can then be tested experimentally

The utility of cognitive plausibility in language acquisition modeling: Evidence from word segmentation. (Manuscript

Abstract The informativity of a computational model of language acquisition is directly related to how closely it approximates the actual acquisition task, sometimes referred to as the model's cognitive plausibility. We suggest that though every computational model necessarily idealizes the modeled task, an informative language acquisition model can aim to be cognitively plausible in multiple ways. We discuss these cognitive plausibility checkpoints in general terms, and then apply them to a case study in word segmentation, investigating a promising Bayesian segmentation strategy. We create a more cognitively plausible model of this learning strategy which uses an age-appropriate unit of perceptual representation, evaluates the model output in terms of its utility, and incorporates cognitive constraints into the inference process. Our more cognitively plausible model of the Bayesian word segmentation strategy not only yields better performance than previous implementations but also shows more strongly the beneficial effect of cognitive constraints on segmentation. One interpretation of this effect is as a synergy between the naive theories of language structure that infants may have and the cognitive constraints that limit the fidelity of their inference processes, where less accurate inference approximations are better when the underlying assumptions about how words are generated are less accurate. More generally, these results highlight the utility of incorporating cognitive plausibility more fully into computational models of language acquisition