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"

12 / 12 papers shown

Title
Deep Model Compression Also Helps Models Capture Ambiguity Hancheol Park Jong C. Park 33 1 0 12 Jun 2023
Preference-grounded Token-level Guidance for Language Model Fine-tuning Shentao Yang Shujian Zhang Congying Xia Yihao Feng Caiming Xiong Mi Zhou 29 23 0 01 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 27 12 0 03 Apr 2023
Sources of Noise in Dialogue and How to Deal with Them Derek Chen Zhou Yu 26 2 0 06 Dec 2022
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 29 53 0 28 Oct 2022
Investigating Reasons for Disagreement in Natural Language Inference Nan-Jiang Jiang M. Marneffe 27 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 27 26 0 10 May 2022
Natural Language Deduction through Search over Statement Compositions Kaj Bostrom Zayne Sprague Swarat Chaudhuri Greg Durrett ReLM LRM 27 46 0 16 Jan 2022
Clean or Annotate: How to Spend a Limited Data Collection Budget Derek Chen Zhou Yu Samuel R. Bowman 37 13 0 15 Oct 2021
Bayesian Attention Modules Xinjie Fan Shujian Zhang Bo Chen Mingyuan Zhou 117 59 0 20 Oct 2020
Calibration of Pre-trained Transformers Shrey Desai Greg Durrett UQLM 246 290 0 17 Mar 2020