activity
20202025
most citedDocument-Level Relation Extraction with Reconstruction

7 citations · 16 across the 6 of their papers we have counts for

collaborators

6 papers

cs.CL2025

DuplexMamba: Enhancing Real-time Speech Conversations with Duplex and Streaming Capabilities

Xiangyu Lu, Wang Xu, Haoyu Wang +5

Real-time speech conversation is essential for natural and efficient human-machine interactions, requiring duplex and streaming capabilities. Traditional Transformer-based conversa…

cs.CL2024★ 2 cited

Ouroboros: Generating Longer Drafts Phrase by Phrase for Faster Speculative Decoding

Weilin Zhao, Yuxiang Huang, Xu Han +7

Speculative decoding is a widely used method that accelerates the generation process of large language models (LLMs) with no compromise in model performance. It achieves this goal…

cs.LG2024★ 3 cited

LoRA-drop: Efficient LoRA Parameter Pruning based on Output Evaluation

Hongyun Zhou, Xiangyu Lu, Wang Xu +3

Low-Rank Adaptation (LoRA) is currently the most commonly used Parameter-efficient fine-tuning (PEFT) method, it introduces auxiliary parameters for each layer to fine-tune the pre…

cs.CL2022★ 1 cited

Document-Level Relation Extraction with Sentences Importance Estimation and Focusing

Wang Xu, Kehai Chen, Lili Mou +1

Document-level relation extraction (DocRE) aims to determine the relation between two entities from a document of multiple sentences. Recent studies typically represent the entire…

cs.CL2021★ 3 cited

Discriminative Reasoning for Document-level Relation Extraction

Wang Xu, Kehai Chen, Tiejun Zhao

Document-level relation extraction (DocRE) models generally use graph networks to implicitly model the reasoning skill (i.e., pattern recognition, logical reasoning, coreference re…

cs.CL2020★ 7 cited

Document-Level Relation Extraction with Reconstruction

Wang Xu, Kehai Chen, Tiejun Zhao

In document-level relation extraction (DocRE), graph structure is generally used to encode relation information in the input document to classify the relation category between each…