2 citations · 4 across the 4 of their papers we have counts for
5 papers · 1 filter
Evaluating Large Language Models for Document-grounded Response Generation in Information-Seeking Dialogues
Norbert Braunschweiler, Rama Doddipatla, Simon Keizer +1
In this paper, we investigate the use of large language models (LLMs) like ChatGPT for document-grounded response generation in the context of information-seeking dialogues. For ev…
Adversarial learning of neural user simulators for dialogue policy optimisation
Simon Keizer, Caroline Dockes, Norbert Braunschweiler +2
Reinforcement learning based dialogue policies are typically trained in interaction with a user simulator. To obtain an effective and robust policy, this simulator should generate…
Dialogue Strategy Adaptation to New Action Sets Using Multi-dimensional Modelling
Simon Keizer, Norbert Braunschweiler, Svetlana Stoyanchev +1
A major bottleneck for building statistical spoken dialogue systems for new domains and applications is the need for large amounts of training data. To address this problem, we ado…
A study on cross-corpus speech emotion recognition and data augmentation
Norbert Braunschweiler, Rama Doddipatla, Simon Keizer +1
Models that can handle a wide range of speakers and acoustic conditions are essential in speech emotion recognition (SER). Often, these models tend to show mixed results when prese…
Open-domain Topic Identification of Out-of-domain Utterances using Wikipedia
A. Augustin, A. Papangelis, M. Kotti +3
Users of spoken dialogue systems (SDS) expect high quality interactions across a wide range of diverse topics. However, the implementation of SDS capable of responding to every con…