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Found 6 papers, 3 papers with code
Suicidal Risk Detection for Military Personnel
We analyze social media for detecting the suicidal risk of military personnel, which is especially crucial for countries with compulsory military service such as the Republic of Korea.
Why Knowledge Distillation Amplifies Gender Bias and How to Mitigate from the Perspective of DistilBERT
Knowledge distillation is widely used to transfer the language understanding of a large model to a smaller model. However, after knowledge distillation, it was found that the smaller model is more biased by gender compared to the source large model. This paper studies what causes gender bias to increase after the knowledge distillation process. Moreover, we suggest applying a variant of the mixup on knowledge distillation, which is used to increase generalizability during the distillation process, not for augmentation. By doing so, we can significantly reduce the gender bias amplification after knowledge distillation. We also conduct an experiment on the GLUE benchmark to demonstrate that even if the mixup is applied, it does not have a significant adverse effect on the model’s performance.
Models and Benchmarks for Representation Learning of Partially Observed Subgraphs
Subgraphs are rich substructures in graphs, and their nodes and edges can be partially observed in real-world tasks.
KOLD: Korean Offensive Language Dataset
Recent directions for offensive language detection are hierarchical modeling, identifying the type and the target of offensive language, and interpretability with offensive span annotation and prediction.
Learning Representations of Partial Subgraphs by Subgraph InfoMax
Subgraphs are important substructures of graphs, but learning their representations has not been studied well.
Mitigating Language-Dependent Ethnic Bias in BERT
Which of the two methods works better depends on the amount of NLP resources available for that language.
