most citedReasoning with Latent Structure Refinement for Document-Level Relation Extraction

28 citations · 45 across the 7 of their papers we have counts for

collaborators

8 papers

cs.CL20217 cited

Speaker-Oriented Latent Structures for Dialogue-Based Relation Extraction

Guoshun Nan, Guoqing Luo, Sicong Leng +2

Dialogue-based relation extraction (DiaRE) aims to detect the structural information from unstructured utterances in dialogues. Existing relation extraction models may be unsatisfa…

cs.MM20214 cited

Fusion with Hierarchical Graphs for Mulitmodal Emotion Recognition

Shuyun Tang, Zhaojie Luo, Guoshun Nan +2

Automatic emotion recognition (AER) based on enriched multimodal inputs, including text, speech, and visual clues, is crucial in the development of emotionally intelligent machines…

cs.CL20211 cited

Uncovering Main Causalities for Long-tailed Information Extraction

Guoshun Nan, Jiaqi Zeng, Rui Qiao +2

Information Extraction (IE) aims to extract structural information from unstructured texts. In practice, long-tailed distributions caused by the selection bias of a dataset, may le…

cs.CV20213 cited

Interventional Video Grounding with Dual Contrastive Learning

Guoshun Nan, Rui Qiao, Yao Xiao +4

Video grounding aims to localize a moment from an untrimmed video for a given textual query. Existing approaches focus more on the alignment of visual and language stimuli with var…

cs.CL2021

Video Corpus Moment Retrieval with Contrastive Learning

Hao Zhang, Aixin Sun, Wei Jing +4

Given a collection of untrimmed and unsegmented videos, video corpus moment retrieval (VCMR) is to retrieve a temporal moment (i.e., a fraction of a video) that semantically corres…

cs.CL2021

Integrating Subgraph-aware Relation and DirectionReasoning for Question Answering

Xu Wang, Shuai Zhao, Bo Cheng +5

Question Answering (QA) models over Knowledge Bases (KBs) are capable of providing more precise answers by utilizing relation information among entities. Although effective, most o…