Publications

Abstract

Linguistic fieldwork is increasingly moving forward from the traditional model of lone fieldworker with a notebook to collaborative projects with key roles for native speakers and other experts and involving the use of different kinds of stimulus-based elicitation methods as well as extensive video documentation. Several cohorts of colleagues and students have been influenced by this inclusive and interdisciplinary view of linguistic fieldwork. We describe the challenges and benefits of doing multi-methods collaborative fieldwork. As linguistics inevitably moves into the direction of multiple methods, interdisciplinarity and team science, now is the time to reflect critically on how best to contribute to a cumulative science of language.

Abstract

The robustness and flexibility of human language is underpinned by a machinery of interactive repair. Repair is deeply intertwined with two core properties of human language: reflexivity (it can communicate about itself) and accountability (it is used to publicly enforce social norms). We review empirical and theoretical advances from across the cognitive sciences that mark interactive repair as a domain of pragmatic universals, a key place to study metacognition in interaction, and a system that enables collective computation. This provides novel insights on the role of repair in comparative cognition, language development and human-computer interaction. As an always-available fallback option and an infrastructure for negotiating social commitments, interactive repair is foundational to the resilience, complexity, and flexibility of human language.

Abstract

The past year has seen a steep rise in generative AI systems that claim to be open. But how open are they really? The question of what counts as open source in generative AI is poised to take on particular importance in light of the upcoming EU AI Act that regulates open source systems differently, creating an urgent need for practical openness assessment. Here we use an evidence-based framework that distinguishes 14 dimensions of openness, from training datasets to scientific and technical documentation and from licensing to access methods. Surveying over 45 generative AI systems (both text and text-to-image), we find that while the term open source is widely used, many models are `open weight' at best and many providers seek to evade scientific, legal and regulatory scrutiny by withholding information on training and fine-tuning data. We argue that openness in generative AI is necessarily composite (consisting of multiple elements) and gradient (coming in degrees), and point out the risk of relying on single features like access or licensing to declare models open or not. Evidence-based openness assessment can help foster a generative AI landscape in which models can be effectively regulated, model providers can be held accountable, scientists can scrutinise generative AI, and end users can make informed decisions.

Abstract

Measures of ‘humanness’, ‘coherence’ or ‘fluency’ are the mainstay of dialogue system evaluation, but they don’t target system capabilities and rarely offer actionable feedback. Reviewing recent work in this domain, we identify an opportunity for evaluation at the level of action sequences, rather than the more commonly targeted levels of whole conversations or single responses. We introduce interactive probes, an evaluation framework inspired by empirical work on social interaction that can help to systematically probe the capabilities of dialogue systems. We sketch some first probes in the domains of tellings and repair, two sequence types ubiquitous in human interaction and challenging for dialogue systems. We argue interactive probing can offer the requisite flexibility to keep up with developments in interactive language technologies and do justice to the open-endedness of action formation and ascription in interaction.

Abstract

Minimal expressions are at the heart of interaction: Interjections like "Huh?" and "Mhm" keep conversations flowing by establishing and reinforcing intersubjectivity among interlocutors. Crosslinguistic research has identified that similar interactional pressures can yield structurally similar words (e.g., to initiate repair across languages). While crosslinguistic comparisons that include signed languages remain uncommon, recent work has revealed similarities in discourse management strategies among signers and speakers that share much of their cultural background. This study contributes a crossmodal comparison of repair initiators and continuers in speakers of English and signers of British Sign Language (BSL). We combine qualitative and quantitative analyses of data from sixteen English speakers and sixteen BSL signers, resulting in the following: First, the interactional infrastructure drawn upon by speakers and signers overwhelmingly relies on behaviors of the head, face, and body; these are used alone or sometimes in combination with verbal elements (i.e., spoken words or manual signs), while verbal strategies alone are rare. Second, discourse management strategies are remarkably similar in form across the two languages: A held eye gaze or freeze-look is the predominant repair initiator and head nodding the main continuer. These results suggest a modality-agnostic preference for visual strategies that do not occupy the primary articulators, one that we propose is founded in recipiency; people maintain the flow of communication following principles of minimal effort and minimal interruption.

Abstract

The vocabularies of natural languages harbour many instances of iconicity, where words show a perceived resemblance between aspects of form and meaning. An open challenge in this domain is how to reconcile different operationalizations of iconicity and link them to an empirically grounded theory. Here we combine three ways of looking at iconicity using a set of 239 iconic words from 5 spoken languages (Japanese, Korean, Semai, Siwu and Ewe). Data on guessing accuracy serves as a baseline measure of probable iconicity and provides variation that we seek to explain and predict using structure-mapping theory and iconicity ratings. We systematically trace a range of cross-linguistically attested form-meaning correspondences in the dataset, yielding a word-level measure of cumulative iconicity that we find to be highly predictive of guessing accuracy. In a rating study, we collect iconicity judgments for all words from 78 participants. The ratings are well-predicted by our measure of cumulative iconicity and also correlate strongly with guessing accuracy, showing that rating tasks offer a scalable method to measure iconicity. Triangulating the measures reveals how structure-mapping can help open the black box of experimental measures of iconicity. While none of the methods is perfect, taken together they provide a well-rounded way to approach the meaning and measurement of iconicity in natural language vocabulary.

Abstract

We welcome Enfield and Zuckerman’s (E&Z’s) rich exposition on how people congregate around shared representations. Moorings are a useful addition to our tools for thinking about signs and their uses. As public fixtures to which actions, statuses, and experiences may be tied, moorings evoke Geertz’s (1973) webs of significance, Millikan’s (2005) public conventions, and Clark’s (2015) common ground, but they add to these accounts a focus on the sign and the promise of understanding in more detail how people come to share and calibrate experiences.

Abstract

Iconic words and signs are characterized by a perceived resemblance between aspects of their form and aspects of their meaning. For example, in English, iconic words include peep and crash, which mimic the sounds they denote, and wiggle and zigzag, which mimic motion. As a semiotic property of words and signs, iconicity has been demonstrated to play a role in word learning, language processing, and language evolution. This paper presents the results of a large-scale norming study for more than 14,000 English words conducted with over 1400 American English speakers. We demonstrate the utility of these ratings by replicating a number of existing findings showing that iconicity ratings are related to age of acquisition, sensory modality, semantic neighborhood density, structural markedness, and playfulness. We discuss possible use cases and limitations of the rating dataset, which is made publicly available.