This page collects some recent talks (videos & slides) given by members of the group. (At the moment, mostly by David Schlangen.)

2022: Norm Participation Grounds Language

The slides for my (DS) paper at “(dis)embodiment, a CLASP conference”,

  • Schlangen, David. 2022. “Norm Participation Grounds Language.” In Proceedings of the 2022 CLASP Conference on (Dis)Embodiment, 62–69. Gothenburg, Sweden: Association for Computational Linguistics. (Schlangen 2022)
  • slides

2022: Are We Nearly There Yet? On Natural Language Understanding and Natural Language Use

I will talk about some of our recent projects. The first, which provides the framing for the talk, starts from the puzzle that while the performance of modern NLP models on quite sophisticated tasks such as those collected in superGLUE (Wang et al., 2019) suggests that the state of “Natural Language Understanding” in machines is very advanced, so-called “intelligent agents” such as Alexa or Siri – presumably similarly state-of-the-art – show little practical language intelligence. As an attempt of explaining this, I sketch a model of language understanding according to which this capability is much richer than what current predictive models cover.

Arguing that understanding Natural Language Use is crucial for approaching Natural Language Understanding, I turn to an analysis of what language users need to be able to do – which is a lot. The motivating question of the second part will be whether all of this can be crammed into (processing) “a single $&!#* vector” (Mooney 2014), and learned from predicting the “next $&!#* word”. I will describe our work on injecting world knowledge into transformer-based conversational language models (Galetzka et al., ACL 2021), our work on testing the (latent) discourse models built by language models for coherence prediction (Beyer et al., NAACL 2021), and very recent work on whether models of “visual dialogue” track public committments similarly to how humans would do it (Madureira & Schlangen, ACL 2022). Connecting to the opening question, I will end with a brief description of a benchmarking paradigm for language use – and hence, language understanding – that I dub the “Cooperative Turing Game”.

2021: From Language Processing to Language Use [DS]

This is a largely theoretical talk, in which I try to develop an argument for a particular research programme in “linguistic AI”. The first step will be to identify the standard research programme in NLP, which is harder as it perhaps should be, as NLP (in its guise as engineering practice) doesn’t tend to state or examine its presuppositions. I take “you can learn ‘natural language understanding’ from observation” and “you can atomise language use into seperately modelled ‘language tasks’ “ to be two such presuppositions, and argue against them, in support for the claim that NLP, as it currently is set up, is limited to classification, transduction and compression (which natural language use goes beyond).

Using ideas from the philosophy of language on the role of norms in (linguistic) behaviour, I examine a number of cases where the straightforward application of NLP models in ways that make the resulting systems appear to be language users leads to problems, which can systematically be analysed as failures in normative behaviour. (Which to a certain degree can be addressed by adding explicit provisions to the system and/or its application context.) Highlighting one particular phenomenon, I argue that the speech act of assertion requires more than just being able to produce declarative sentences, even if they may seem situationally adequate; what is missing is a whole host of interactional capabilities.

This will bring me to an analysis of the prototypical interaction type, situated real-time interaction, as being built on what I call “the four cornerstones of linguistic intelligence”: incremental processing, incremental learning, conversational grounding, and multimodal grounding; which separately and collectively form the targets of this research programme. As a further positive contribution, I argue for a focus on re-usable research objects (in addition to and beyond machine learning model architectures or “foundation models”), such as a) cognitive architectures, b) experiment environments, c) dialogue games. I close with a sketch of an evaluation framework for artificial language users: Collaborative Turing Games.

  • Dec. 2021 version (partially presented at Pitt NLP seminar), slides

2021: Targeting the Benchmark: On Methodology in Current NLP Research [DS]

The pre-recorded talk accompanying my short paper at ACL 2021 (Schlangen 2021) (which is a revised version of and supersedes the earlier version on ArXiv; which in turn is a development out of an earlier longer paper (Schlangen 2019)). In this paper, I try to take a “meta” perspective, trying to make explicit some consequences of and presuppositions behind typical practices around the research objects models, datasets, and tasks. In particular, I argue that the selection of the latter, the tasks, should be guided less by pre-theoretical notions of what makes for a challenging test, and more by considerations of how the tasks bring out theoretical assumptions and how they connect to each other.

2021: All Interaction is Situated, All Language is Grounded [DS]

Invited talk at the German conference on speech and signal processing, ESSV 2021. I used this opportunity to put some of our work on incremental processing and language & vision into context, making the claim that interactive systems are always situated (if only in a joint notion of time), and language is always grounded. I used the latter to diagnose some of what’s problematic with using language models as chat agents.

  • (no video, unfortunately)
  • slides

2021: What Should / Could Computational Linguistics be About? [DS]

A short (well, 1 hour) meditation on what Computational Linguistics is, what it could be, and how we could get from the former to the latter. I used the opportunity of having been invited to address a conference of German students of linguistics to introduce my version of comp ling. It’s sort of a continuation of the ideas from (Schlangen 2019) and (Schlangen 2020), where I tried to formulate some dissatistfaction with the focus on instances of language use that can be framed as one-shot mappings from input to output.

2020: A Model of Situated Discourse Processing [DS]

This is the 2020 version of a longer-term project of mine (DS) on constructing a model of situated discourse processing that is both useful as an analytical tool, clarifying thoughts about how discourse processing works, as well as implementable. This talk was presented as semdial 2020. It is a cleaned up version of the long and rambling series of talks I inflicted on the audience when I was “international chair” at LabEx Universite de Paris in 2019. (Although in those talks, I presented many more experiments, which are still waiting to be written up properly.) The website of the project is here, where you can also find a link to all 8 hours of those lectures…

  • Schlangen, David. 2020. “An Outline of a Model of Situated Discourse Representation and Processing.” In Proceedings of Semdial 2020 (WatchDial). Brandeis University / Internet. (Schlangen 2020)
  • slides