7 citations · 10 across the 4 of their papers we have counts for
4 papers
Effective and Efficient Conversation Retrieval for Dialogue State Tracking with Implicit Text Summaries
Seanie Lee, Jianpeng Cheng, Joris Driesen +2
Few-shot dialogue state tracking (DST) with Large Language Models (LLM) relies on an effective and efficient conversation retriever to find similar in-context examples for prompt l…
LUCID: LLM-Generated Utterances for Complex and Interesting Dialogues
Joe Stacey, Jianpeng Cheng, John Torr +5
Spurred by recent advances in Large Language Models (LLMs), virtual assistants are poised to take a leap forward in terms of their dialogue capabilities. Yet a major bottleneck to…
Intelligent Assistant Language Understanding On Device
Cecilia Aas, Hisham Abdelsalam, Irina Belousova +20
It has recently become feasible to run personal digital assistants on phones and other personal devices. In this paper we describe a design for a natural language understanding sys…
Conversational Semantic Parsing for Dialog State Tracking
Jianpeng Cheng, Devang Agrawal, Hector Martinez Alonso +12
We consider a new perspective on dialog state tracking (DST), the task of estimating a user's goal through the course of a dialog. By formulating DST as a semantic parsing task ove…