ResearchTrend.AI
Learning a Cost-Effective Annotation Policy for Question Answering
v1v2 (latest)

Learning a Cost-Effective Annotation Policy for Question Answering

Bernhard Kratzwald
Stefan Feuerriegel
Huan Sun
ArXiv (abs)PDFHTML

Papers citing "Learning a Cost-Effective Annotation Policy for Question Answering"

3 / 3 papers shown
Title
Multi-Source Test-Time Adaptation as Dueling Bandits for Extractive Question Answering
Multi-Source Test-Time Adaptation as Dueling Bandits for Extractive Question Answering
Hai Ye
Qizhe Xie
Hwee Tou Ng
88
8
0
11 Jun 2023
Simulating Bandit Learning from User Feedback for Extractive Question Answering
Simulating Bandit Learning from User Feedback for Extractive Question Answering
Ge Gao
Eunsol Choi
Yoav Artzi
85
14
0
18 Mar 2022
Learning with Different Amounts of Annotation: From Zero to Many Labels
Learning with Different Amounts of Annotation: From Zero to Many Labels
Shujian Zhang
Chengyue Gong
Eunsol Choi
81
32
0
09 Sep 2021