Interjections are, in Felix Ameka’s memorable formulation, “the universal yet neglected part of speech” (1992). They are rarely the subject of historical, typological or comparative research in linguistics, and they are notably underrepresented in descriptive grammars. As grammars are the main source of data for typologists, this is of course a perfect example of a self-reinforcing feedback loop. How can we break this trend?

There is a minor industry in speech science and NLP devoted to detecting and removing disfluencies. In some of our recent work we’re showing this adversely impacts voice user interfaces. Here I review a case where the hemming and hawing is the point — and where removing it adversely impacts our ability to make sense of what people do in interaction.

The last time I blindly accepted an invitation to speak was in 2012, when I was invited to an exclusive round table on the future of linguistics. As a fresh postdoc I was honoured and bedazzled. When the programme was circulated, I got a friendly email from a colleague asking me how I’d ended up there, and whether I thought the future of linguistics was to be all male. Turns out the round table was exclusive in more than one sense.

This is a the second part in a two part series of peer commentary on a recent preprint.

One of the benefits of today’s preprint culture is that it is possible to provide constructive critique of pending work before it is out. This post is written in that spirit.

In a recent BBS paper, Clark & Fischer propose that people see social robots as interactive depictions and that this explains some aspects of people’s behaviour towards them. We note that they leave unexamined the notion of “social” in social robots: the question of how technologies like this become enmeshed in human sociality.

A lot of our recent work revolves around working with conversational data, and one thing that’s struck me is that there are no easy ways to create compelling visualizations. In the Elementary Particles of Conversations project we’re aiming to change that. Here’s a sneak peek.

Will synthetic text generators usher in a new age of creative thinking? The remarkable fluency of large language models may make them interesting tools for rapidly exploring semantic and stylistic spaces, yet the deceptive ease with which they generate output also provides countless new ways of appropriating ideas and erasing authorship.

It’s easy to forget amidst a rising tide of synthetic text, but language is not actually about strings of words, and language scientists would do well not to chain themselves to models that presume so. For apt and timely commentary we turn to Bronislaw Malinowski

We don’t generally see PhD dissertations as an exciting genre to read, and that is wholly our loss. As the publishing landscape of academia is fast being homogenised, the thesis is one of the last places where we have a chance to see the unalloyed brilliance of up and coming researchers. Let me show you using three examples of remarkable theses I have come across in the past years.

Sketches, visualizations and other forms of externalizing cognition play a prominent role in the work of just about any scientist. It’s why we love using blackboards, whiteboards, notebooks and scraps of paper.