29 citations · 75 across the 17 of their papers we have counts for
5 papers · 1 filter
User Simulation with Large Language Models for Evaluating Task-Oriented Dialogue
Sam Davidson, Salvatore Romeo, Raphael Shu +4
One of the major impediments to the development of new task-oriented dialogue (TOD) systems is the need for human evaluation at multiple stages and iterations of the development pr…
DiactTOD: Learning Generalizable Latent Dialogue Acts for Controllable Task-Oriented Dialogue Systems
Qingyang Wu, James Gung, Raphael Shu +1
Dialogue act annotations are important to improve response generation quality in task-oriented dialogue systems. However, it can be challenging to use dialogue acts to control resp…
Pre-training Intent-Aware Encoders for Zero- and Few-Shot Intent Classification
Mujeen Sung, James Gung, Elman Mansimov +5
Intent classification (IC) plays an important role in task-oriented dialogue systems. However, IC models often generalize poorly when training without sufficient annotated examples…
Intent Induction from Conversations for Task-Oriented Dialogue Track at DSTC 11
James Gung, Raphael Shu, Emily Moeng +6
With increasing demand for and adoption of virtual assistants, recent work has investigated ways to accelerate bot schema design through the automatic induction of intents or the i…
Conversation Style Transfer using Few-Shot Learning
Shamik Roy, Raphael Shu, Nikolaos Pappas +4
Conventional text style transfer approaches focus on sentence-level style transfer without considering contextual information, and the style is described with attributes (e.g., for…