Search Results for author: Zhendong Mao

Found 15 papers, 11 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

$k$NN Prompting: Beyond-Context Learning with Calibration-Free Nearest Neighbor Inference

1 code implementation24 Mar 2023 Benfeng Xu, Quan Wang, Zhendong Mao, Yajuan Lyu, Qiaoqiao She, Yongdong Zhang

In-Context Learning (ICL), which formulates target tasks as prompt completion conditioned on in-context demonstrations, has become the prevailing utilization of LLMs.

Intra-class Adaptive Augmentation with Neighbor Correction for Deep Metric Learning

1 code implementation29 Nov 2022 Zheren Fu, Zhendong Mao, Bo Hu, An-An Liu, Yongdong Zhang

They have overlooked the wide characteristic changes of different classes and can not model abundant intra-class variations for generations.

Metric Learning Retrieval

ABINet++: Autonomous, Bidirectional and Iterative Language Modeling for Scene Text Spotting

1 code implementation19 Nov 2022 Shancheng Fang, Zhendong Mao, Hongtao Xie, Yuxin Wang, Chenggang Yan, Yongdong Zhang

In this paper, we argue that the limited capacity of language models comes from 1) implicit language modeling; 2) unidirectional feature representation; and 3) language model with noise input.

Language Modelling Scene Text Recognition +1

UniRel: Unified Representation and Interaction for Joint Relational Triple Extraction

1 code implementation16 Nov 2022 Wei Tang, Benfeng Xu, Yuyue Zhao, Zhendong Mao, Yifeng Liu, Yong Liao, Haiyong Xie

Relational triple extraction is challenging for its difficulty in capturing rich correlations between entities and relations.

Relation Extraction

Improving Chinese Spelling Check by Character Pronunciation Prediction: The Effects of Adaptivity and Granularity

1 code implementation20 Oct 2022 Jiahao Li, Quan Wang, Zhendong Mao, Junbo Guo, Yanyan Yang, Yongdong Zhang

In this paper, we consider introducing an auxiliary task of Chinese pronunciation prediction (CPP) to improve CSC, and, for the first time, systematically discuss the adaptivity and granularity of this auxiliary task.

Lesion-Aware Transformers for Diabetic Retinopathy Grading

no code implementations CVPR 2021 Rui Sun, Yihao Li, Tianzhu Zhang, Zhendong Mao, Feng Wu, Yongdong Zhang

First, to the best of our knowledge, this is the first work to formulate lesion discovery as a weakly supervised lesion localization problem via a transformer decoder.

Diabetic Retinopathy Grading

Image Captioning with Context-Aware Auxiliary Guidance

no code implementations10 Dec 2020 Zeliang Song, Xiaofei Zhou, Zhendong Mao, Jianlong Tan

Image captioning is a challenging computer vision task, which aims to generate a natural language description of an image.

Image Captioning

Curriculum Learning for Natural Language Understanding

no code implementations ACL 2020 Benfeng Xu, Licheng Zhang, Zhendong Mao, Quan Wang, Hongtao Xie, Yongdong Zhang

With the great success of pre-trained language models, the pretrain-finetune paradigm now becomes the undoubtedly dominant solution for natural language understanding (NLU) tasks.

Natural Language Understanding

Graph Structured Network for Image-Text Matching

1 code implementation CVPR 2020 Chunxiao Liu, Zhendong Mao, Tianzhu Zhang, Hongtao Xie, Bin Wang, Yongdong Zhang

The GSMN explicitly models object, relation and attribute as a structured phrase, which not only allows to learn correspondence of object, relation and attribute separately, but also benefits to learn fine-grained correspondence of structured phrase.

Cross-Modal Retrieval Text Matching

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