.png){kind=icon}
返回
Automatic Noise Generation and Reduction for Text Classification
.png){kind=icon}
PRE
AI
DOI：10.1109/TASLP.2023.3325135.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Label noise is an important issue in machine learning, which might lead to negative influences on various tasks. Given that real benchmarks for evaluation of noise reduction methods are limited, plenty of studies construct pseudo noisy data to verify their proposed methods. However, very few works have realized the rationality of the noise generation strategies. If the generated pseudo datasets are biased, their final conclusions might also be problematic. In this work, we focus on text classification of natural language processing (NLP) to investigate various pseudo noise generation methods, which is the first work of this line for NLP. In particular, we compare the noise generated with crowdsourcing noise, a kind of real noise as gold-standard, to evaluate these noise generation methods. After then, we measure and compare the performance of representative noise reduction methods respectively based on the data of crowdsourcing and our top-ranked pseudo noisy generation strategies. We conduct experiments on five text classification datasets, offering detailed comparison results as well as discussions.
Keyword:
Noise generation
noise reduction
pseudo noise
crowdsourcing
期刊
I
IF:
5.1
论文数:
2.6K
被引数:
1.1W
机构
引用论文
Polymorphism for direction of coil in Partula suturalis: Behavioural isolation and positive frequency dependent selection.png)
Heredity
OAAI
HeredityIF0
OAAI分享收藏分享收藏Two decades of statistical language modeling: Where do we go from here?统计语言建模二十年: 我们何去何从？PROCEEDINGS OF THE IEEE
OAAI
PROCEEDINGS OF THE IEEEIF25.9
OAAI分享收藏分享收藏Eras: Improving the quality control in the annotation process for Natural Language Processing tasks
PREAI
分享收藏
查看更多内容