Volume 4, Issue 1

The syntax-prosody interface concerns the relationship between syntactic and prosodic constituent structure. This paper provides an overview of theoretical advances in research on the syntax-prosody interface. Current theoretical work is situated historically, and is framed in light of the central research questions in the field, including (a) to what extent prosodic structure can be used as a diagnostic for syntactic constituent structure, (b) the significance of recursion in prosodic theory, and (c) how mismatches between syntactic and prosodic constituent structure are modeled in different approaches to the syntax-prosody interface. The paper concludes with a discussion of the current state of the field and directions for future research.

The paper presents an overview of The Database of Eurasian Phonological Inventories —a new information resource and analytical tool for research in the field of distributional phonological typology, theoretical phonology, and areal linguistics.

There are synchronic sources of data that can provide an additional line of evidence which may be useful in reconstructing sound changes: patterns of sound change in progress and experimentally induced changes, variation in production, natural errors in production and perception, experimentally elicited errors in perception and production, and experiments and simulations of iterated learning. This article surveys existing studies that have made use of such evidence in support of sound changes and reviews limitations of experimental methods and factors to consider when designing experiments to use these parallels to inform sound change. To demonstrate the parallels between patterns in synchronic data and sound changes, a sample typology of diachronic developments was compared with patterns of categorical errors from experimentally elicited misperception in adverse listening conditions and errors of perception and production in natural speech. All of these correlations are highly significant, demonstrating the potential of such synchronic data as a source of parallels to provide evidence for reconstructed sound changes.

The Yale Grammatical Diversity Project approaches the empirical domain of North American English from the perspective of generative microcomparative syntax. In addition to eliciting judgments from speakers of particular varieties, we also conduct large-scale surveys, map the results of those surveys geographically, conduct statistical tests taking geography and other social variables into account, and look for theoretically significant linguistic correlations. In all cases, we do this with the primary goal of understanding variation between speakers at the individual level. While our goals and methodologies are informed by our theoretical perspective, we expect that our work and results will be of interest to linguists working in other frameworks and even to the public more generally. This article outlines the goals and methodologies of the project and describes in broad strokes some of the results obtained so far, as well as some of the ways we have shared our findings with others, inside and outside academia.

This paper takes a detailed look at some popular models of evolution used in contemporary diachronic linguistic research, focusing on the continuous-time Markov model, a particularly popular choice. I provide an exposition of the math underlying the CTM model, seldom discussed in linguistic papers. I show that in some work, a lack of explicit reference to the underlying computation creates some difficulty in interpreting results, particularly in the domain of ancestral state reconstruction. I conclude by adumbrating some ways in which linguists may be able to exploit these models to investigate a suite of factors which may influence diachronic linguistic change.

Experience-based approaches to language hold that individuals become sensitive to distributed emergent phenomena in their linguistic experience. The purpose of this paper is to bring together experience-based perspectives from the domains of cognitive psychology and linguistics. First, we present an overview of the cognitive processes that underpin experience-based learning, and review the cognitive biases that have been attributed to the emergence of distributional regularities in language. We then discuss the P-chain (Dell, G. S. & F. Chang. 2014. The P-chain: Relating sentence production and its disorders to comprehension and acquisition. Philosophical Transactions of the Royal Society of London. Series B, Biological Sciences 369(20120394). 1–9.), an influential experience-based framework for experience-based theory in psycholinguistics, and present data from bilingual speakers to substantiate the assumptions of the model. Our goal is to focus on language usage in bilinguals to illustrate how individuals can become attuned to linguistic variation in the input and how this input can act as constraining information with critical psycholinguistic implications.

The present article focuses on two questions: (i) How do listeners infer the social identity of a speaker based on how they choose to describe the world? (ii) Are these inferences informed by similar principles to those motivating the social significance of linguistic phenomena in other domains of the grammar? We address this issue by exploring the social meaning of imprecision (Lasersohn 1999): speakers’ well-attested tendency to apply varying degrees of deviation from the truth when reporting facts (e.g., describing a car as going 70 MPH, instead of 69). Based on results from a social perception study, we found (i) that a high degree of precision is associated with a constellation of both favorable and unfavorable qualities; (ii) that different linguistic cues to signal precision differentially affect the social meaning of the utterance; (iii) and that most such qualities bear a striking resemblance to those associated to variation in other realms – e.g., the hyper/hypo-articulation of sounds. We take this as evidence that semantic variation can be socially meaningful across the specific lexical items in which it manifests itself, and that such social meanings can be linguistically motivated by similar principles across different domains of the grammar.

Social information such as ethnicity affects metalinguistic judgments, speech perception and evaluation. This study tested whether previously reported negative effects of perceived East-Asian ethnicity on language comprehension and accentedness ratings would also be found for Moroccan ethnicity and in a socio-cultural environment with a population used to being and communicating with nonnative speakers. The results showed that accentedness ratings and comprehension scores do not depend upon the ethnicity of the speaker. We then tested whether the effect would change under adverse listening conditions and found an effect of perceived ethnicity on accentedness ratings but not on comprehension scores, suggesting that the effect of ethnicity on language comprehension is not altered under adverse listening conditions. Effects of ethnicity on accentedness ratings thus replicate previous findings, but only under suboptimal listening conditions. Although the effect of ethnicity on comprehension was not replicated in regards to Moroccan ethnicity and in a linguistically experienced population, negative correlations between accentedness ratings and the corresponding comprehension scores underlie the contribution of listeners’ characteristics to the comprehension and evaluation of nonnative speech.

Most automatic sentiment analyses of texts tend to only employ a simple positive-negative polarity to classify emotions. In this paper, I illustrate a more fine-grained automatic sentiment analysis [Jockers, Matthew. 2016. Introduction to the Syuzhet package. https://cran.r-project.org/web/packages/syuzhet/vignettes/syuzhet-vignette.html (accessed 07 March 2017).; Mohammad, Saif M. & Peter D. Turney. 2013. Crowd sourcing a word-emotion association lexicon. Computational Intelligence 29(3). 436–465.] that is based on a classification of human emotions that has been put forward by psychological research [Plutchik, Robert. 1994. The psychology and biology of emotion . New York, NY: HarperCollins College Publishers.]. The advantages of this approach are illustrated by a sample study that analyses the emotional sentiment of the campaign speeches of the two main candidates of the 2016 US presidential election.

We expect generative models of language to correctly predict surface forms from underlying forms, but morphologically complex words, especially compounds, can exhibit idiosyncratic outputs, which require an extra lexical listing. This results in (a) a poorer Minimum Description Length of our model and (b) failure of a grammar to capture patterning among exceptions. To solve an instance of this problem, we examine pitch-accent patterns of 2-mora-2-mora Japanese Yamato (native) noun-noun compounds, hitherto considered semi-predictable but which show gradient tendencies among constituents to trigger a particular accent pattern. In the framework of Gradient Symbolic Computation (Smolensky, Paul & Matthew Goldrick. 2015. Gradient symbolic computation. LSA Summer Institute Workshop. Chicago.), a type of harmonic grammar which allows partially activated feature values and weighted constraints, such gradient patterns can be captured through the additive combination of coalescing features on each conjunct, which results in a pitch accent when the summed activations surpass a threshold determined by the grammar. The ability of this framework to completely predict these semi-regular patterns holds promise that it can also explain similar kinds of patterns in other languages.

Research into the multimodal dimensions of human communication faces a set of distinctive methodological challenges. Collecting the datasets is resource-intensive, analysis often lacks peer validation, and the absence of shared datasets makes it difficult to develop standards. External validity is hampered by small datasets, yet large datasets are intractable. Red Hen Lab spearheads an international infrastructure for data-driven multimodal communication research, facilitating an integrated cross-disciplinary workflow. Linguists, communication scholars, statisticians, and computer scientists work together to develop research questions, annotate training sets, and develop pattern discovery and machine learning tools that handle vast collections of multimodal data, beyond the dreams of previous researchers. This infrastructure makes it possible for researchers at multiple sites to work in real-time in transdisciplinary teams. We review the vision, progress, and prospects of this research consortium.