.png){kind=icon}
返回
Federated Learning for Exploiting Annotators' Disagreements in Natural Language Processing
.png){kind=icon}
OA
AI
DOI：10.1162/tacl_a_00664.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
The annotation of ambiguous or subjective NLP tasks is usually addressed by various annotators. In most datasets, these annotations are aggregated into a single ground truth. However, this omits divergent opinions of annotators, hence missing individual perspectives. We propose FLEAD (Federated Learning for Exploiting Annotators' Disagreements), a methodology built upon federated learning to independently learn from the opinions of all the annotators, thereby leveraging all their underlying information without relying on a single ground truth. We conduct an extensive experimental study and analysis in diverse text classification tasks to show the contribution of our approach with respect to mainstream approaches based on majority voting and other recent methodologies that also learn from annotator disagreements.
期刊
T
IF:
6.9
论文数:
486
被引数:
5.7K
机构
引用论文
Matrix Effects in the Detection of Pb and Ba in Soils Using Laser-Induced Breakdown Spectroscopy使用激光诱导击穿光谱检测土壤中Pb和Ba的基质效应
PREAI
分享收藏3D multi-step travel time tomography: Imaging the local, deep velocity structure of Rabaul volcano, Papua New Guinea3D多步旅行时间层析成像: 对巴布亚新几内亚拉包尔火山的局部深层速度结构进行成像
PREAI
分享收藏分享收藏Status and associated characteristics of HIV disclosure among people living with HIV/AIDS in Liangshan, China.png)
Medicine
OAAI
MedicineIF0
OAAI分享收藏EFFECT OF TESTOSTERONE ON PROTEIN SYNTHESIS AND CONTRACTILITY OF THE LEVATOR ANI MUSCLE OF THE RAT
PREAI
分享收藏
查看更多内容