ResearchTrend.AI
  • Papers
  • Communities
  • Events
  • Blog
  • Pricing
Papers
Communities
Social Events
Terms and Conditions
Pricing
Parameter LabParameter LabTwitterGitHubLinkedInBlueskyYoutube

© 2025 ResearchTrend.AI, All rights reserved.

  1. Home
  2. Papers
  3. 2104.04470
  4. Cited By
Did they answer? Subjective acts and intents in conversational discourse

Did they answer? Subjective acts and intents in conversational discourse

9 April 2021
Elisa Ferracane
Greg Durrett
Junjie Li
K. Erk
ArXiv (abs)PDFHTMLGithub (6★)

Papers citing "Did they answer? Subjective acts and intents in conversational discourse"

17 / 17 papers shown
Title
CoBRA: Quantifying Strategic Language Use and LLM Pragmatics
CoBRA: Quantifying Strategic Language Use and LLM Pragmatics
Anshun Asher Zheng
Junyi Jessy Li
David Beaver
40
0
0
01 Jun 2025
"I Never Said That": A dataset, taxonomy and baselines on response
  clarity classification
"I Never Said That": A dataset, taxonomy and baselines on response clarity classification
Konstantinos Thomas
Giorgos Filandrianos
Maria Lymperaiou
Chrysoula Zerva
Giorgos Stamou
63
0
0
20 Sep 2024
PEFT-U: Parameter-Efficient Fine-Tuning for User Personalization
PEFT-U: Parameter-Efficient Fine-Tuning for User Personalization
Christopher Clarke
Yuzhao Heng
Lingjia Tang
Jason Mars
55
4
0
25 Jul 2024
Self-Knowledge Distillation for Learning Ambiguity
Self-Knowledge Distillation for Learning Ambiguity
Hancheol Park
Soyeong Jeong
Sukmin Cho
Jong C. Park
71
1
0
14 Jun 2024
Designing NLP Systems That Adapt to Diverse Worldviews
Designing NLP Systems That Adapt to Diverse Worldviews
Claudiu Creanga
Liviu P. Dinu
48
0
0
18 May 2024
D3CODE: Disentangling Disagreements in Data across Cultures on
  Offensiveness Detection and Evaluation
D3CODE: Disentangling Disagreements in Data across Cultures on Offensiveness Detection and Evaluation
Aida Mostafazadeh Davani
Mark Díaz
Dylan K. Baker
Vinodkumar Prabhakaran
74
10
0
16 Apr 2024
Cost-Efficient Subjective Task Annotation and Modeling through Few-Shot
  Annotator Adaptation
Cost-Efficient Subjective Task Annotation and Modeling through Few-Shot Annotator Adaptation
Preni Golazizian
Ali Omrani
Alireza S. Ziabari
Morteza Dehghani
49
1
0
21 Feb 2024
Different Tastes of Entities: Investigating Human Label Variation in
  Named Entity Annotations
Different Tastes of Entities: Investigating Human Label Variation in Named Entity Annotations
Siyao Peng
Zihang Sun
Sebastian Loftus
Barbara Plank
71
3
0
02 Feb 2024
Analyzing Dataset Annotation Quality Management in the Wild
Analyzing Dataset Annotation Quality Management in the Wild
Jan-Christoph Klie
Richard Eckart de Castilho
Iryna Gurevych
88
26
0
16 Jul 2023
Using Natural Language Explanations to Rescale Human Judgments
Using Natural Language Explanations to Rescale Human Judgments
Manya Wadhwa
Jifan Chen
Junyi Jessy Li
Greg Durrett
88
8
0
24 May 2023
You Are What You Annotate: Towards Better Models through Annotator
  Representations
You Are What You Annotate: Towards Better Models through Annotator Representations
Naihao Deng
Xinliang Frederick Zhang
Siyang Liu
Winston Wu
Lu Wang
Rada Mihalcea
63
21
0
24 May 2023
Design Choices for Crowdsourcing Implicit Discourse Relations: Revealing
  the Biases Introduced by Task Design
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
125
12
0
03 Apr 2023
Sources of Noise in Dialogue and How to Deal with Them
Sources of Noise in Dialogue and How to Deal with Them
Derek Chen
Zhou Yu
62
2
0
06 Dec 2022
The 'Problem' of Human Label Variation: On Ground Truth in Data,
  Modeling and Evaluation
The 'Problem' of Human Label Variation: On Ground Truth in Data, Modeling and Evaluation
Barbara Plank
93
100
0
04 Nov 2022
Investigating Reasons for Disagreement in Natural Language Inference
Investigating Reasons for Disagreement in Natural Language Inference
Nan-Jiang Jiang
M. Marneffe
72
27
0
07 Sep 2022
ALLSH: Active Learning Guided by Local Sensitivity and Hardness
ALLSH: Active Learning Guided by Local Sensitivity and Hardness
Shujian Zhang
Chengyue Gong
Xingchao Liu
Pengcheng He
Weizhu Chen
Mingyuan Zhou
102
26
0
10 May 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
1