28 citations · 81 across the 11 of their papers we have counts for
6 papers · 1 filter
Reducing and Exploiting Data Augmentation Noise through Meta Reweighting Contrastive Learning for Text Classification
Guanyi Mou, Yichuan Li, Kyumin Lee
Data augmentation has shown its effectiveness in resolving the data-hungry problem and improving model's generalization ability. However, the quality of augmented data can be varie…
An Effective, Robust and Fairness-aware Hate Speech Detection Framework
Guanyi Mou, Kyumin Lee
With the widespread online social networks, hate speeches are spreading faster and causing more damage than ever before. Existing hate speech detection methods have limitations in…
SWE2: SubWord Enriched and Significant Word Emphasized Framework for Hate Speech Detection
Guanyi Mou, Pengyi Ye, Kyumin Lee
Hate speech detection on online social networks has become one of the emerging hot topics in recent years. With the broad spread and fast propagation speed across online social net…
HABERTOR: An Efficient and Effective Deep Hatespeech Detector
Thanh Tran, Yifan Hu, Changwei Hu +4
We present our HABERTOR model for detecting hatespeech in large scale user-generated content. Inspired by the recent success of the BERT model, we propose several modifications to…
Hierarchical Evidence Set Modeling for Automated Fact Extraction and Verification
Shyam Subramanian, Kyumin Lee
Automated fact extraction and verification is a challenging task that involves finding relevant evidence sentences from a reliable corpus to verify the truthfulness of a claim. Exi…
Learning from Fact-checkers: Analysis and Generation of Fact-checking Language
Nguyen Vo, Kyumin Lee
In fighting against fake news, many fact-checking systems comprised of human-based fact-checking sites (e.g., snopes.com and politifact.com) and automatic detection systems have be…