44 citations · 63 across the 11 of their papers we have counts for
6 papers · 1 filter
FurChat: An Embodied Conversational Agent using LLMs, Combining Open and Closed-Domain Dialogue with Facial Expressions
Neeraj Cherakara, Finny Varghese, Sheena Shabana +9
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 usin…
The Dangers of trusting Stochastic Parrots: Faithfulness and Trust in Open-domain Conversational Question Answering
Sabrina Chiesurin, Dimitris Dimakopoulos, Marco Antonio Sobrevilla Cabezudo +4
Large language models are known to produce output which sounds fluent and convincing, but is also often wrong, e.g. "unfaithful" with respect to a rationale as retrieved from a kno…
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…
SemEval-2023 Task 11: Learning With Disagreements (LeWiDi)
Elisa Leonardelli, Alexandra Uma, Gavin Abercrombie +6
NLP datasets annotated with human judgments are rife with disagreements between the judges. This is especially true for tasks depending on subjective judgments such as sentiment an…
Why Robust Natural Language Understanding is a Challenge
Marco Casadio, Ekaterina Komendantskaya, Verena Rieser +4
With the proliferation of Deep Machine Learning into real-life applications, a particular property of this technology has been brought to attention: robustness Neural Networks noto…
Crowd-sourcing NLG Data: Pictures Elicit Better Data
Jekaterina Novikova, Oliver Lemon, Verena Rieser
Recent advances in corpus-based Natural Language Generation (NLG) hold the promise of being easily portable across domains, but require costly training data, consisting of meaning…