Learning with Different Amounts of Annotation: From Zero to Many Labels

9 September 2021

Papers citing "Learning with Different Amounts of Annotation: From Zero to Many Labels"

10 / 10 papers shown

Title
Deep Model Compression Also Helps Models Capture Ambiguity Hancheol Park Jong C. Park 27 1 0 12 Jun 2023
Design Choices for Crowdsourcing Implicit Discourse Relations: Revealing the Biases Introduced by Task Design Valentina Pyatkin Frances Yung Merel C. J. Scholman Reut Tsarfaty Ido Dagan Vera Demberg 19 12 0 03 Apr 2023
The 'Problem' of Human Label Variation: On Ground Truth in Data, Modeling and Evaluation Barbara Plank 30 97 0 04 Nov 2022
Stop Measuring Calibration When Humans Disagree Joris Baan Wilker Aziz Barbara Plank Raquel Fernández 24 53 0 28 Oct 2022
Investigating Reasons for Disagreement in Natural Language Inference Nan-Jiang Jiang M. Marneffe 16 26 0 07 Sep 2022
ALLSH: Active Learning Guided by Local Sensitivity and Hardness Shujian Zhang Chengyue Gong Xingchao Liu Pengcheng He Weizhu Chen Mingyuan Zhou 25 26 0 10 May 2022
Natural Language Deduction through Search over Statement Compositions Kaj Bostrom Zayne Sprague Swarat Chaudhuri Greg Durrett ReLM LRM 24 46 0 16 Jan 2022
Clean or Annotate: How to Spend a Limited Data Collection Budget Derek Chen Zhou Yu Samuel R. Bowman 27 13 0 15 Oct 2021
Bayesian Attention Modules Xinjie Fan Shujian Zhang Bo Chen Mingyuan Zhou 111 59 0 20 Oct 2020
Calibration of Pre-trained Transformers Shrey Desai Greg Durrett UQLM 243 289 0 17 Mar 2020