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
1 code implementation • 24 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.
1 code implementation • 29 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.
1 code implementation • 19 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.
1 code implementation • 16 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.
Ranked #1 on Relation Extraction on WebNLG
1 code implementation • 20 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.
no code implementations • 3 Sep 2022 • Mengqi Huang, Zhendong Mao, Penghui Wang, Quan Wang, Yongdong Zhang
Text-to-image generation aims at generating realistic images which are semantically consistent with the given text.
1 code implementation • CVPR 2022 • Kun Zhang, Zhendong Mao, Quan Wang, Yongdong Zhang
Image-text matching, as a fundamental task, bridges the gap between vision and language.
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.
2 code implementations • CVPR 2021 • Shancheng Fang, Hongtao Xie, Yuxin Wang, Zhendong Mao, Yongdong Zhang
Additionally, based on the ensemble of iterative predictions, we propose a self-training method which can learn from unlabeled images effectively.
3 code implementations • 20 Feb 2021 • Benfeng Xu, Quan Wang, Yajuan Lyu, Yong Zhu, Zhendong Mao
Our experiments demonstrate the usefulness of the proposed entity structure and the effectiveness of SSAN.
Ranked #3 on Relation Extraction on DocRED
1 code implementation • 17 Dec 2020 • Xi Zhu, Zhendong Mao, Chunxiao Liu, Peng Zhang, Bin Wang, Yongdong Zhang
Our method can compensate for the data biases by generating balanced data without introducing external annotations.
no code implementations • 10 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.
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.
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.
Ranked #13 on Cross-Modal Retrieval on Flickr30k