4 citations · 9 across the 14 of their papers we have counts for
6 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…
Measuring and Mitigating Constraint Violations of In-Context Learning for Utterance-to-API Semantic Parsing
Shufan Wang, Sebastien Jean, Sailik Sengupta +3
In executable task-oriented semantic parsing, the system aims to translate users' utterances in natural language to machine-interpretable programs (API calls) that can be executed…
NatCS: Eliciting Natural Customer Support Dialogues
James Gung, Emily Moeng, Wesley Rose +3
Despite growing interest in applications based on natural customer support conversations, there exist remarkably few publicly available datasets that reflect the expected character…
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…