15 citations · 43 across the 13 of their papers we have counts for
15 papers
Weakly Supervised Data Augmentation Through Prompting for Dialogue Understanding
Maximillian Chen, Alexandros Papangelis, Chenyang Tao +5
Dialogue understanding tasks often necessitate abundant annotated data to achieve good performance and that presents challenges in low-resource settings. To alleviate this barrier,…
What is wrong with you?: Leveraging User Sentiment for Automatic Dialog Evaluation
Sarik Ghazarian, Behnam Hedayatnia, Alexandros Papangelis +2
Accurate automatic evaluation metrics for open-domain dialogs are in high demand. Existing model-based metrics for system response evaluation are trained on human annotated data, w…
Training Conversational Agents with Generative Conversational Networks
Yen-Ting Lin, Alexandros Papangelis, Seokhwan Kim +1
Rich, open-domain textual data available on the web resulted in great advancements for language processing. However, while that data may be suitable for language processing tasks,…
"How Robust r u?": Evaluating Task-Oriented Dialogue Systems on Spoken Conversations
Seokhwan Kim, Yang Liu, Di Jin +4
Most prior work in dialogue modeling has been on written conversations mostly because of existing data sets. However, written dialogues are not sufficient to fully capture the natu…
Generative Conversational Networks
Alexandros Papangelis, Karthik Gopalakrishnan, Aishwarya Padmakumar +3
Inspired by recent work in meta-learning and generative teaching networks, we propose a framework called Generative Conversational Networks, in which conversational agents learn to…
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…