9 citations · 20 across the 10 of their papers we have counts for
14 papers
MCR-Data2vec 2.0: Improving Self-supervised Speech Pre-training via Model-level Consistency Regularization
Ji Won Yoon, Seok Min Kim, Nam Soo Kim
Self-supervised learning (SSL) has shown significant progress in speech processing tasks. However, despite the intrinsic randomness in the Transformer structure, such as dropout va…
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,…
Towards Textual Out-of-Domain Detection without In-Domain Labels
Di Jin, Shuyang Gao, Seokhwan Kim +2
In many real-world settings, machine learning models need to identify user inputs that are out-of-domain (OOD) so as to avoid performing wrong actions. This work focuses on a chall…
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…
Commonsense-Focused Dialogues for Response Generation: An Empirical Study
Pei Zhou, Karthik Gopalakrishnan, Behnam Hedayatnia +5
Smooth and effective communication requires the ability to perform latent or explicit commonsense inference. Prior commonsense reasoning benchmarks (such as SocialIQA and Commonsen…