Pragmatism and the predictive mind

Predictive processing and its apparent commitment to explaining cognition in terms of Bayesian inference over hierarchical generative models seems to flatly contradict the pragmatist conception of mind and experience. Against this, I argue that this appearance results from philosophical overlays at odd with the science itself, and that the two frameworks are in fact well-positioned for mutually beneficial theoretical exchange. Specifically, I argue: first, that predictive processing illuminates pragmatism’s commitment to both the primacy of pragmatic coping in accounts of the mind and the profound organism-relativity of experience; second, that this pragmatic, “narcissistic” character of prediction error minimization undermines its ability to explain the distinctive normativity of intentionality; and third, that predictive processing therefore mandates an extra-neural account of intentional content of exactly the sort that pragmatism’s communitarian vision of human thought can provide

Computational Sociolinguistics: A Survey

Language is a social phenomenon and variation is inherent to its social nature. Recently, there has been a surge of interest within the computational linguistics (CL) community in the social dimension of language. In this article we present a survey of the emerging field of "Computational Sociolinguistics" that reflects this increased interest. We aim to provide a comprehensive overview of CL research on sociolinguistic themes, featuring topics such as the relation between language and social identity, language use in social interaction and multilingual communication. Moreover, we demonstrate the potential for synergy between the research communities involved, by showing how the large-scale data-driven methods that are widely used in CL can complement existing sociolinguistic studies, and how sociolinguistics can inform and challenge the methods and assumptions employed in CL studies. We hope to convey the possible benefits of a closer collaboration between the two communities and conclude with a discussion of open challenges.Comment: To appear in Computational Linguistics. Accepted for publication: 18th February, 201

Language (Technology) is Power: A Critical Survey of "Bias" in NLP

We survey 146 papers analyzing "bias" in NLP systems, finding that their motivations are often vague, inconsistent, and lacking in normative reasoning, despite the fact that analyzing "bias" is an inherently normative process. We further find that these papers' proposed quantitative techniques for measuring or mitigating "bias" are poorly matched to their motivations and do not engage with the relevant literature outside of NLP. Based on these findings, we describe the beginnings of a path forward by proposing three recommendations that should guide work analyzing "bias" in NLP systems. These recommendations rest on a greater recognition of the relationships between language and social hierarchies, encouraging researchers and practitioners to articulate their conceptualizations of "bias"---i.e., what kinds of system behaviors are harmful, in what ways, to whom, and why, as well as the normative reasoning underlying these statements---and to center work around the lived experiences of members of communities affected by NLP systems, while interrogating and reimagining the power relations between technologists and such communities

Culture as Non-Consensus: Exploring Coherence Among Native Speakers’ Perceptions of German Expressions of Affection

From early in their learning experience, foreign language (FL) learners at American universities explore socio-cultural connotations that, it is argued, are signified by FL words. Textbook authors and teachers follow an implicit canon of difference, a list of iconic words that over time—and without the benefit of empirical evidence—have come to represent essential differences in outlook between their native and the FL culture (Kubota, 2004). Despite the fast progression of the theory of teaching culture in FL learning (Kramsch, 2015; Risager, 2015), large empirical gaps remain. Todate, there is little evidence that native speakers (NSs) of the FL perceive their cultural practices, including the cultural contexts in which language is used, homogenously enough to warrant their status as cultural traits. Using the example of expressions of affection, this exploratory study drew on qualitative and quantitative questionnaire data to investigate whether German NSs’ (N=52) accounts of their own and of most fellow Germans’ language behavior converged enough to derive a comprehensive and reliable cultural norm. Results indicated a lack of consensus among German NSs’ self-reported views, eluding the assumption of a pertinent community-specific norm. Implications for FL teaching and learning, as well as directions for future research, are discussed

ILR School Ph.D. Dissertations

Compiled by Susan LaCette.ILRSchoolPhD.pdf: 4022 downloads, before Oct. 1, 2020

The Issue of Hybridization of Discursive Practices

The discourse research, which was reduced to analysing such linguistic problems as the text and the conditions of its generation, gradually gave way to analysing discourse as a social phenomenon. In the 90s of the last century, the status of discourse analysis was determined as an interdisciplinary science. In all fundamental research, the central theme is the concept of discourse as a derivative of social practice and its connection with the social structure

Situational Transformations: the offensive-izing of an email message and the public-ization of offensiveness

This paper raises concerns about the tenor of 21st century interaction by identifying a tendency whereby relatively innocuous, canonically private communication is transformed into public communication deemed offensive enough to attract institutional or legal sanction. To understand examples of this tendency, it applies Goffman’s architecture of interaction to email communication and proposes the notion of situational transformation to encapsulate reframing processes involving footing, face and participation framework. Through these processes (to which, it is shown, the email medium is especially vulnerable) and a discourse of civility, the private becomes public and opposition becomes offence

Sociolinguistically Driven Approaches for Just Natural Language Processing

Natural language processing (NLP) systems are now ubiquitous. Yet the benefits of these language technologies do not accrue evenly to all users, and indeed they can be harmful; NLP systems reproduce stereotypes, prevent speakers of non-standard language varieties from participating fully in public discourse, and re-inscribe historical patterns of linguistic stigmatization and discrimination. How harms arise in NLP systems, and who is harmed by them, can only be understood at the intersection of work on NLP, fairness and justice in machine learning, and the relationships between language and social justice. In this thesis, we propose to address two questions at this intersection: i) How can we conceptualize harms arising from NLP systems?, and ii) How can we quantify such harms? We propose the following contributions. First, we contribute a model in order to collect the first large dataset of African American Language (AAL)-like social media text. We use the dataset to quantify the performance of two types of NLP systems, identifying disparities in model performance between Mainstream U.S. English (MUSE)- and AAL-like text. Turning to the landscape of bias in NLP more broadly, we then provide a critical survey of the emerging literature on bias in NLP and identify its limitations. Drawing on work across sociology, sociolinguistics, linguistic anthropology, social psychology, and education, we provide an account of the relationships between language and injustice, propose a taxonomy of harms arising from NLP systems grounded in those relationships, and propose a set of guiding research questions for work on bias in NLP. Finally, we adapt the measurement modeling framework from the quantitative social sciences to effectively evaluate approaches for quantifying bias in NLP systems. We conclude with a discussion of recent work on bias through the lens of style in NLP, raising a set of normative questions for future work

An Affiliative Model of Early Lexical Learning

In defining the language acquisition problem, traditional models abstract away effects of variability, defining the learner as acquiring a single language variety, which is spoken homogeneously by their speech community. However, infants are exposed to as many unique varieties of speech as they are speakers. Adult sociolinguistic competence is also characterized by the capacity to employ and interpret non-phonological linguistic distinctions which are associated with different social groups, including ‘code-switching’ or ‘style-shifting’ between languages and speech registers. This dissertation presents a model of infant lexical acquisition which assumes that learners monitor linguistic sources for variation in reliability. This model is adapted from Shafto, Eaves, Navarro, and Perfors (2012) which the authors used to describe the behavior of preschool children in selecting sources to learn labels from in K. Corriveau and Harris (2009) and M. Corriveau and Harris (2009). I show that this probabilistic model effectively simulates two experiments from the literature on preverbal infants’ perception of labeling, Rost and McMurray (2009) and Koenig and Echols (2003). Evidence suggests that the receptiveness of preverbal infants to novel lexical items is correlated with infant beliefs regarding the informant’s knowledgeability and social group membership. These simulations demonstrate that language learners may well be recruiting processes of epistemic trust to guide lexical acquisition much earlier than previously suggested. We should therefore expect even very young listeners to respond differently to dialects not solely as a function of exposure, but also as a function of attitudes towards the speech determined by the quality of that exposure. Developmental differences between populations in attention to non-linguistic affiliative cues are therefore expected to emerge early and have significant effects on language outcomes. Measures of online language proficiency may be vulnerable to significant bias owing to the activation of sociolinguistic biases in the presentation of test items. Differences in the breadth or specificity of listener preferences for speakers in turn predict differences in task complexity for learners of standard and non-standard dialects. A new research program in early sociophonetic perception, uniting accounts of selective trust with language learning has the potential to deepen understanding of both typical and disordered language development