activity
20172025
most citedOverview of the TREC 2020 deep learning track

117 citations · 511 across the 34 of their papers we have counts for

collaborators
Showing 2023Show all

5 papers · 1 filter

cs.IR2023★ 1 cited

A Survey on Asking Clarification Questions Datasets in Conversational Systems

Hossein A. Rahmani, Xi Wang, Yue Feng +3

The ability to understand a user's underlying needs is critical for conversational systems, especially with limited input from users in a conversation. Thus, in such a domain, Aski…

cs.CL2023★ 3 cited

Towards Asking Clarification Questions for Information Seeking on Task-Oriented Dialogues

Yue Feng, Hossein A. Rahmani, Aldo Lipani +1

Task-oriented dialogue systems aim at providing users with task-specific services. Users of such systems often do not know all the information about the task they are trying to acc…

cs.CL2023

Modeling User Satisfaction Dynamics in Dialogue via Hawkes Process

Fanghua Ye, Zhiyuan Hu, Emine Yilmaz

Dialogue systems have received increasing attention while automatically evaluating their performance remains challenging. User satisfaction estimation (USE) has been proposed as an…

cs.IR2023★ 2 cited

Query-specific Variable Depth Pooling via Query Performance Prediction towards Reducing Relevance Assessment Effort

Debasis Ganguly, Emine Yilmaz

Due to the massive size of test collections, a standard practice in IR evaluation is to construct a 'pool' of candidate relevant documents comprised of the top-k documents retrieve…

cs.IR2023

Task2KB: A Public Task-Oriented Knowledge Base

Procheta Sen, Xi Wang, Ruiqing Xu +1

Search engines and conversational assistants are commonly used to help users complete their every day tasks such as booking travel, cooking, etc. While there are some existing data…