Search Results for author: Aimin Yang

Found 7 papers, 1 papers with code

HateDebias: On the Diversity and Variability of Hate Speech Debiasing

no code implementations7 Jun 2024 Nankai Lin, Hongyan Wu, Zhengming Chen, Zijian Li, Lianxi Wang, Shengyi Jiang, Dong Zhou, Aimin Yang

To further meet the variability (i. e., the changing of bias attributes in datasets), we reorganize datasets to follow the continuous learning setting.

Hate Speech Detection

An interpretability framework for Similar case matching

no code implementations4 Apr 2023 Nankai Lin, Haonan Liu, Jiajun Fang, Dong Zhou, Aimin Yang

Subsequently, our framework aligns the corresponding sentences in two legal cases to provide evidence of similarity.


Model and Evaluation: Towards Fairness in Multilingual Text Classification

no code implementations28 Mar 2023 Nankai Lin, Junheng He, Zhenghang Tang, Dong Zhou, Aimin Yang

The multilingual text representation module uses a multilingual pre-trained language model to represent the text, the language fusion module makes the semantic spaces of different languages tend to be consistent through contrastive learning, and the text debiasing module uses contrastive learning to make the model unable to identify sensitive attributes' information.

Contrastive Learning Fairness +4

An Effective Deployment of Contrastive Learning in Multi-label Text Classification

no code implementations1 Dec 2022 Nankai Lin, Guanqiu Qin, Jigang Wang, Aimin Yang, Dong Zhou

We explore the effectiveness of contrastive learning for multi-label text classification tasks by the employment of these novel losses and provide a set of baseline models for deploying contrastive learning techniques on specific tasks.

Contrastive Learning Multi-Label Classification +3

A Chinese Spelling Check Framework Based on Reverse Contrastive Learning

no code implementations25 Oct 2022 Nankai Lin, Hongyan Wu, Sihui Fu, Shengyi Jiang, Aimin Yang

Inspired by contrastive learning, we present a novel framework for Chinese spelling checking, which consists of three modules: language representation, spelling check and reverse contrastive learning.

Contrastive Learning

CL-XABSA: Contrastive Learning for Cross-lingual Aspect-based Sentiment Analysis

1 code implementation2 Apr 2022 Nankai Lin, Yingwen Fu, Xiaotian Lin, Aimin Yang, Shengyi Jiang

In the distillation XABSA task, we further explore the comparative effectiveness of different data (source dataset, translated dataset, and code-switched dataset).

Aspect-Based Sentiment Analysis Aspect-Based Sentiment Analysis (ABSA) +2

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