8 citations · 32 across the 36 of their papers we have counts for
5 papers · 1 filter
Enhancing Abstractiveness of Summarization Models through Calibrated Distillation
Hwanjun Song, Igor Shalyminov, Hang Su +3
Sequence-level knowledge distillation reduces the size of Seq2Seq models for more efficient abstractive summarization. However, it often leads to a loss of abstractiveness in summa…
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…
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…
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…