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Found 10 papers, 1 papers with code
NLP Verification: Towards a General Methodology for Certifying Robustness
We propose a number of practical NLP methods that can help to identify the effects of the embedding gap; and in particular we propose the metric of falsifiability of semantic subpspaces as another fundamental metric to be reported as part of the NLP verification pipeline.
FurChat: An Embodied Conversational Agent using LLMs, Combining Open and Closed-Domain Dialogue with Facial Expressions
We demonstrate an embodied conversational agent that can function as a receptionist and generate a mixture of open and closed-domain dialogue along with facial expressions, by using a large language model (LLM) to develop an engaging conversation.
Mirages: On Anthropomorphism in Dialogue Systems
In this paper, we discuss the linguistic factors that contribute to the anthropomorphism of dialogue systems and the harms that can arise, including reinforcing gender stereotypes and notions of acceptable language.
iLab at SemEval-2023 Task 11 Le-Wi-Di: Modelling Disagreement or Modelling Perspectives?
There are two competing approaches for modelling annotator disagreement: distributional soft-labelling approaches (which aim to capture the level of disagreement) or modelling perspectives of individual annotators or groups thereof.
ANTONIO: Towards a Systematic Method of Generating NLP Benchmarks for Verification
In particular, many known neural network verification methods that work for computer vision and other numeric datasets do not work for NLP.
Missing Information, Unresponsive Authors, Experimental Flaws: The Impossibility of Assessing the Reproducibility of Previous Human Evaluations in NLP
We report our efforts in identifying a set of previous human evaluations in NLP that would be suitable for a coordinated study examining what makes human evaluations in NLP more/less reproducible.
Fillers in Spoken Language Understanding: Computational and Psycholinguistic Perspectives
Disfluencies (i. e. interruptions in the regular flow of speech), are ubiquitous to spoken discourse.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
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Studying Alignment in a Collaborative Learning Activity via Automatic Methods: The Link Between What We Say and Do
We also find that well-performing teams verbalise the marker "oh" more when they are behaviourally aligned, compared to other times in the dialogue; showing that this marker is an important cue in alignment.
The importance of fillers for text representations of speech transcripts
While being an essential component of spoken language, fillers (e. g."um" or "uh") often remain overlooked in Spoken Language Understanding (SLU) tasks.
