Search Results for author: Yong Zhu

Found 16 papers, 6 papers with code

EmRel: Joint Representation of Entities and Embedded Relations for Multi-triple Extraction

1 code implementation NAACL 2022 Benfeng Xu, Quan Wang, Yajuan Lyu, Yabing Shi, Yong Zhu, Jie Gao, Zhendong Mao

Multi-triple extraction is a challenging task due to the existence of informative inter-triple correlations, and consequently rich interactions across the constituent entities and relations. While existing works only explore entity representations, we propose to explicitly introduce relation representation, jointly represent it with entities, and novelly align them to identify valid triples. We perform comprehensive experiments on document-level relation extraction and joint entity and relation extraction along with ablations to demonstrate the advantage of the proposed method.

Document-level Relation Extraction Joint Entity and Relation Extraction +2

Dynamic Multistep Reasoning based on Video Scene Graph for Video Question Answering

no code implementations NAACL 2022 Jianguo Mao, Wenbin Jiang, Xiangdong Wang, Zhifan Feng, Yajuan Lyu, Hong Liu, Yong Zhu

Then, it performs multistep reasoning for better answer decision between the representations of the question and the video, and dynamically integrate the reasoning results.

Question Answering Video Question Answering +1

Learn and Review: Enhancing Continual Named Entity Recognition via Reviewing Synthetic Samples

no code implementations Findings (ACL) 2022 Yu Xia, Quan Wang, Yajuan Lyu, Yong Zhu, Wenhao Wu, Sujian Li, Dai Dai

However, the existing method depends on the relevance between tasks and is prone to inter-type confusion. In this paper, we propose a novel two-stage framework Learn-and-Review (L&R) for continual NER under the type-incremental setting to alleviate the above issues. Specifically, for the learning stage, we distill the old knowledge from teacher to a student on the current dataset.

Continual Named Entity Recognition named-entity-recognition +2

Improving Video Retrieval by Adaptive Margin

no code implementations9 Mar 2023 Feng He, Qi Wang, Zhifan Feng, Wenbin Jiang, Yajuan Lv, Yong Zhu, Xiao Tan

While most video retrieval methods overlook that phenomenon, we propose an adaptive margin changed with the distance between positive and negative pairs to solve the aforementioned issue.

Retrieval Video Retrieval

Mixture of Experts for Biomedical Question Answering

no code implementations15 Apr 2022 Damai Dai, Wenbin Jiang, Jiyuan Zhang, Weihua Peng, Yajuan Lyu, Zhifang Sui, Baobao Chang, Yong Zhu

In this paper, in order to alleviate the parameter competition problem, we propose a Mixture-of-Expert (MoE) based question answering method called MoEBQA that decouples the computation for different types of questions by sparse routing.

Question Answering

Complex Evolutional Pattern Learning for Temporal Knowledge Graph Reasoning

1 code implementation ACL 2022 Zixuan Li, Saiping Guan, Xiaolong Jin, Weihua Peng, Yajuan Lyu, Yong Zhu, Long Bai, Wei Li, Jiafeng Guo, Xueqi Cheng

Furthermore, these models are all trained offline, which cannot well adapt to the changes of evolutional patterns from then on.

Building Chinese Biomedical Language Models via Multi-Level Text Discrimination

1 code implementation14 Oct 2021 Quan Wang, Songtai Dai, Benfeng Xu, Yajuan Lyu, Yong Zhu, Hua Wu, Haifeng Wang

In this work we introduce eHealth, a Chinese biomedical PLM built from scratch with a new pre-training framework.

Domain Adaptation

Link Prediction on N-ary Relational Facts: A Graph-based Approach

1 code implementation Findings (ACL) 2021 Quan Wang, Haifeng Wang, Yajuan Lyu, Yong Zhu

The key to our approach is to represent the n-ary structure of a fact as a small heterogeneous graph, and model this graph with edge-biased fully-connected attention.

Knowledge Graphs Link Prediction

Multi-view Classification Model for Knowledge Graph Completion

no code implementations Asian Chapter of the Association for Computational Linguistics 2020 Wenbin Jiang, Mengfei Guo, Yufeng Chen, Ying Li, Jinan Xu, Yajuan Lyu, Yong Zhu

This paper describes a novel multi-view classification model for knowledge graph completion, where multiple classification views are performed based on both content and context information for candidate triple evaluation.

Classification Knowledge Graph Completion

CoKE: Contextualized Knowledge Graph Embedding

3 code implementations6 Nov 2019 Quan Wang, Pingping Huang, Haifeng Wang, Songtai Dai, Wenbin Jiang, Jing Liu, Yajuan Lyu, Yong Zhu, Hua Wu

This work presents Contextualized Knowledge Graph Embedding (CoKE), a novel paradigm that takes into account such contextual nature, and learns dynamic, flexible, and fully contextualized entity and relation embeddings.

Knowledge Graph Embedding Link Prediction +1

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