Relative clauses as a benchmark for Minimalist parsing

Minimalist grammars have been used recently in a series of papers to explain well-known contrasts in human sentence processing in terms of subtle structural differences. These proposals combine a top-down parser with complexity metrics that relate parsing difficulty to memory usage. So far, though, there has been no large-scale exploration of the space of viable metrics. Building on this earlier work, we compare the ability of 1600 metrics to derive several processing effects observed with relative clauses, many of which have been proven difficult to unify. We show that among those 1600 candidates, a few metrics (and only a few) can provide a unified account of all these contrasts. This is a welcome result for two reasons: First, it provides a novel account of extensively studied psycholinguistic data. Second, it significantly limits the number of viable metrics that may be applied to other phenomena, thus reducing theoretical indeterminacy

Processing French RCs with Postverbal Subjects in a Minimalist Parser

Computational models with explicit assumptions about the connection between syntactic representations and processing difficulty can help strengthen bridges between theoretical linguistics and psycholinguistics. In this sense, a model based on Stabler’s parser for Minimalist grammars (MGs; Stabler 2013) has been shown to predict a variety of off-line processing pref- erences, by exploiting complexity metrics tracking how syntactic structure affects memory load (Graf et al. 2017:a.o.). This model provides an interpretable linking theory between fine-grained syntactic structure in the generative tradition and precise sentence processing results, through transparently specified notions of cognitive cost. Here, we build on recent work on the processing of non-canonical word order constructions in Italian (De Santo 2021), and explore the model’s performance on off-line results for cases of subject inversion within restrictive relative clauses (RCs) in French. This provides an opportunity to probe the sensitivity of the approach to small but critical syntactic differences across similar constructions cross-linguistically

An MG Parsing View into the Processing of Subject and Object Relative Clauses in Basque

Stabler (2013)\u27s top-down parser for Minimalist grammars has been used to account for a variety of off-line processing preferences, with measures of memory load sensitive to subtle structural details. This paper expands the model\u27s empirical coverage to ergative languages by looking at the processing asymmetries reported for Basque relative clauses. Our results show that the model predicts a subject over object preference as identified in the relevant psycholinguistic literature

Evaluating Structural Economy Claims in Relative Clause Attachment

Grillo and Costa (2014) argue for a pseudo- relative (PR) first account of relative clause attachment preferences (RC) such that, when faced with a sentence ambiguous between a PR and a RC interpretation, the parser prefers committing to a PR structure first, thus giving rise to what looks like a high-attachment preference. One possible explanation for this parsing choice is in terms of simplicity of the PR structure, and overall economy principles. Here, we evaluate this hypothesis by testing the predictions of a parser for Minimalist grammars for PR and RC structures in Italian. We discuss the relevance of our results for PR-first explanations of the cross-linguistic variability of RC attachment biases, and highlight the role that computational models can play in evaluating the cognitive plausibility of economy considerations tied to fine-grained structural analyses

A Minimalist Approach to Facilitatory Effects in Stacked Relative Clauses

A top-down parser for Minimalist grammars (MGs; Stabler, 2013) can successfully predict a variety of off-line processing preferences, via metrics linking parsing behavior to memory load (Kobele et al., 2013; Gerth, 2015; Graf et al., 2017). The increasing empirical coverage of this model is intriguing, given its close association to modern minimalist syntax. Recently however, Zhang (2017) has argued that this framework is unable to account for a set of complexity profiles reported for English and Mandarin Chinese stacked relative clauses. Based on these observations, this paper proposes extensions to this model implementing a notion of memory reactivation, in the form of memory metrics sensitive to repetitions of movement features. We then show how these metrics derive the correct pre- dictions for the stacked RC processing contrast

Processing Advantages of End-weight

Previous research has established that English end-weight configurations, where sentence components of greater grammatical complexity appear at the ends of sentences, demonstrate processing advantages over alternative word orders. To evaluate these processing advantages, I analyze how a Minimalist Grammar (MG) parser generates syntactic structures for different word orders. The parser\u27s behavior suggests that end-weight configurations require fewer memory resources for parsing than alternative structures. This memory load difference accounts for the end-weight advantage in processing. The results highlight the validity of the MG processing approach as a linking theory connecting syntactic structures to behavioral observations. Additionally, the results have implications on the structure and processing of languages where an ``initial-weight\u27\u27 is preferred

MG Parsing as a Model of Gradient Acceptability in Syntactic Islands

It is well-known that the acceptability judgments at the core of current syntactic theories are continuous. However, an open debate is whether the source of such gradience is situated in the grammar itself, or can be derived from extra-grammatical factors. In this paper, we propose the use of a top-down parser for Minimalist grammars (Stabler, 2013; Kobele et al., 2013; Graf et al., 2017), as a formal model of how gradient acceptability can arise from categorical grammars. As a test case, we target the acceptability judgments for island effects collected by Sprouse et al. (2012a)

TOWARDS CHARACTERIZING INCREMENTAL STRUCTURE BUILDING DURING SENTENCE COMPREHENSION

Language comprehension involves incrementally processing sequences of words and generating expectations about upcoming words based on prior context. One of the steps involved in incremental processing is incremental structure building --- i.e., determining the relationship between the words in a sentence as the sentence unfolds. To understand how comprehenders build incremental structures, it is necessary to understand what structures comprehenders build in the first place and why. This dissertation includes three projects that tackle these what and why questions by studying incremental structure building in sentences with reduced relative clauses as a case study. The first project proposes a method for characterizing what incremental structures human comprehenders build. This method involves three steps: first, implement hypotheses from generative syntax about the abstract structure of sentences in a novel computational model; second, use the model to generate quantitative behavioral predictions; and third, test these predictions using a novel web-based experimental paradigm. Applying this approach, we compared two competing theoretical hypotheses about the structure of reduced relative clauses --- Whiz-Deletion and Participial-Phrase --- and demonstrated that the Whiz-Deletion account better characterizes the incremental structures that human comprehenders build. The second project studies why the incremental structures that comprehenders construct can change depending on the environment they are in by testing the following widely debated hypothesis: comprehenders maintain probability distributions over the structures they expect to encounter and rapidly update these distributions to match the statistics of their current environment. Based on a large-scaled reading experiment, we find evidence in support of this hypothesis, but also explain why prior work might have failed to find such support. The third project proposes a method for characterizing what incremental structures Artificial Neural Networks build when processing sentences. Applying this method, we demonstrated that the incremental structures these networks build, like the structures built by human comprehenders, is better characterized by the Whiz-Deletion account than the Participial-Phrase account. Thus, by making it possible to compare the incremental structures that these networks build to the structures that humans build, this method in turn makes it possible to test hypotheses about why humans build the structures they do. I propose several directions for future work which involve applying the methods proposed in these projects to study other phenomena beyond reduced relative clauses