8 citations · 29 across the 12 of their papers we have counts for
6 papers · 1 filter
FurNav: Development and Preliminary Study of a Robot Direction Giver
Bruce W. Wilson, Yann Schlosser, Rayane Tarkany +4
When giving directions to a lost-looking tourist, would you first reference the street-names, cardinal directions, landmarks, or simply tell them to walk five hundred metres in one…
iLab at SemEval-2023 Task 11 Le-Wi-Di: Modelling Disagreement or Modelling Perspectives?
Nikolas Vitsakis, Amit Parekh, Tanvi Dinkar +3
There are two competing approaches for modelling annotator disagreement: distributional soft-labelling approaches (which aim to capture the level of disagreement) or modelling pers…
Mirages: On Anthropomorphism in Dialogue Systems
Gavin Abercrombie, Amanda Cercas Curry, Tanvi Dinkar +2
Automated dialogue or conversational systems are anthropomorphised by developers and personified by users. While a degree of anthropomorphism may be inevitable due to the choice of…
ANTONIO: Towards a Systematic Method of Generating NLP Benchmarks for Verification
Marco Casadio, Luca Arnaboldi, Matthew L. Daggitt +5
Verification of machine learning models used in Natural Language Processing (NLP) is known to be a hard problem. In particular, many known neural network verification methods that…
Missing Information, Unresponsive Authors, Experimental Flaws: The Impossibility of Assessing the Reproducibility of Previous Human Evaluations in NLP
Anya Belz, Craig Thomson, Ehud Reiter +39
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/le…
Fillers in Spoken Language Understanding: Computational and Psycholinguistic Perspectives
Tanvi Dinkar, Chloé Clavel, Ioana Vasilescu
Disfluencies (i.e. interruptions in the regular flow of speech), are ubiquitous to spoken discourse. Fillers ("uh", "um") are disfluencies that occur the most frequently compared t…