3 citations · 3 across the 7 of their papers we have counts for
7 papers
Learning from Relevant Subgoals in Successful Dialogs using Iterative Training for Task-oriented Dialog Systems
Magdalena Kaiser, Patrick Ernst, György Szarvas
Task-oriented Dialog (ToD) systems have to solve multiple subgoals to accomplish user goals, whereas feedback is often obtained only at the end of the dialog. In this work, we prop…
Calibrating Verbalized Probabilities for Large Language Models
Cheng Wang, Gyuri Szarvas, Georges Balazs +2
Calibrating verbalized probabilities presents a novel approach for reliably assessing and leveraging outputs from black-box Large Language Models (LLMs). Recent methods have demons…
Few Shot Rationale Generation using Self-Training with Dual Teachers
Aditya Srikanth Veerubhotla, Lahari Poddar, Jun Yin +2
Self-rationalizing models that also generate a free-text explanation for their predicted labels are an important tool to build trustworthy AI applications. Since generating explana…
Deploying a Retrieval based Response Model for Task Oriented Dialogues
Lahari Poddar, György Szarvas, Cheng Wang +3
Task-oriented dialogue systems in industry settings need to have high conversational capability, be easily adaptable to changing situations and conform to business constraints. Thi…
Taming Continuous Posteriors for Latent Variational Dialogue Policies
Marin Vlastelica, Patrick Ernst, György Szarvas
Utilizing amortized variational inference for latent-action reinforcement learning (RL) has been shown to be an effective approach in Task-oriented Dialogue (ToD) systems for optim…
Transformer Uncertainty Estimation with Hierarchical Stochastic Attention
Jiahuan Pei, Cheng Wang, György Szarvas
Transformers are state-of-the-art in a wide range of NLP tasks and have also been applied to many real-world products. Understanding the reliability and certainty of transformer mo…
Attention Guided Dialogue State Tracking with Sparse Supervision
Shuailong Liang, Lahari Poddar, Gyuri Szarvas
Existing approaches to Dialogue State Tracking (DST) rely on turn level dialogue state annotations, which are expensive to acquire in large scale. In call centers, for tasks like m…