10 citations · 17 across the 5 of their papers we have counts for
5 papers
Enhancing the Preference Extractor in Multi-turn Dialogues: From Annotating Disasters to Accurate Preference Extraction
Cheng Wang, ziru Liu, Pengcheng Tang +3
Identifying user preferences in dialogue systems is a pivotal aspect of providing satisfying services. Current research shows that using large language models (LLMs) to fine-tune a…
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…
C2-CRS: Coarse-to-Fine Contrastive Learning for Conversational Recommender System
Yuanhang Zhou, Kun Zhou, Wayne Xin Zhao +3
Conversational recommender systems (CRS) aim to recommend suitable items to users through natural language conversations. For developing effective CRSs, a major technical issue is…
Learning Slice-Aware Representations with Mixture of Attentions
Cheng Wang, Sungjin Lee, Sunghyun Park +3
Real-world machine learning systems are achieving remarkable performance in terms of coarse-grained metrics like overall accuracy and F-1 score. However, model improvement and deve…
Handling Long-Tail Queries with Slice-Aware Conversational Systems
Cheng Wang, Sun Kim, Taiwoo Park +5
We have been witnessing the usefulness of conversational AI systems such as Siri and Alexa, directly impacting our daily lives. These systems normally rely on machine learning mode…