most citedDocument-Level Relation Extraction with Relation Correlation Enhancement

2 citations · 2 across the 5 of their papers we have counts for

collaborators

5 papers

cs.CL2024

Leveraging Grammar Induction for Language Understanding and Generation

Jushi Kai, Shengyuan Hou, Yusheng Huang +1

Grammar induction has made significant progress in recent years. However, it is not clear how the application of induced grammar could enhance practical performance in downstream t…

cs.AI2023

I2SRM: Intra- and Inter-Sample Relationship Modeling for Multimodal Information Extraction

Yusheng Huang, Zhouhan Lin

Multimodal information extraction is attracting research attention nowadays, which requires aggregating representations from different modalities. In this paper, we present the Int…

cs.IR2023★ 2 cited

Document-Level Relation Extraction with Relation Correlation Enhancement

Yusheng Huang, Zhouhan Lin

Document-level relation extraction (DocRE) is a task that focuses on identifying relations between entities within a document. However, existing DocRE models often overlook the cor…

cs.LG2023

Asymmetric Polynomial Loss For Multi-Label Classification

Yusheng Huang, Jiexing Qi, Xinbing Wang +1

Various tasks are reformulated as multi-label classification problems, in which the binary cross-entropy (BCE) loss is frequently utilized for optimizing well-designed models. Howe…

cs.CL2023

Text Classification in the Wild: a Large-scale Long-tailed Name Normalization Dataset

Jiexing Qi, Shuhao Li, Zhixin Guo +5

Real-world data usually exhibits a long-tailed distribution,with a few frequent labels and a lot of few-shot labels. The study of institution name normalization is a perfect applic…