3 citations · 4 across the 7 of their papers we have counts for
5 papers · 1 filter
Learning to generate and corr- uh I mean repair language in real-time
Arash Eshghi, Arash Ashrafzadeh
In conversation, speakers produce language incrementally, word by word, while continuously monitoring the appropriateness of their own contribution in the dynamically unfolding con…
No that's not what I meant: Handling Third Position Repair in Conversational Question Answering
Vevake Balaraman, Arash Eshghi, Ioannis Konstas +1
The ability to handle miscommunication is crucial to robust and faithful conversational AI. People usually deal with miscommunication immediately as they detect it, using highly sy…
'What are you referring to?' Evaluating the Ability of Multi-Modal Dialogue Models to Process Clarificational Exchanges
Javier Chiyah-Garcia, Alessandro Suglia, Arash Eshghi +1
Referential ambiguities arise in dialogue when a referring expression does not uniquely identify the intended referent for the addressee. Addressees usually detect such ambiguities…
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…
Bootstrapping incremental dialogue systems: using linguistic knowledge to learn from minimal data
Dimitrios Kalatzis, Arash Eshghi, Oliver Lemon
We present a method for inducing new dialogue systems from very small amounts of unannotated dialogue data, showing how word-level exploration using Reinforcement Learning (RL), co…