1 code implementation • EMNLP 2020 • Zixiang Ding, Rui Xia, Jianfei Yu
To tackle these shortcomings, we propose two joint frameworks for ECPE: 1) multi-label learning for the extraction of the cause clauses corresponding to the specified emotion clause (CMLL) and 2) multi-label learning for the extraction of the emotion clauses corresponding to the specified cause clause (EMLL).
Ranked #3 on Emotion-Cause Pair Extraction on ECPE
no code implementations • 24 Sep 2023 • Guo-qing Jiang, Jinlong Liu, Zixiang Ding, Lin Guo, Wei Lin
As models for nature language processing (NLP), computer vision (CV) and recommendation systems (RS) require surging computation, a large number of GPUs/TPUs are paralleled as a large batch (LB) to improve training throughput.
no code implementations • 7 Aug 2023 • Bin Yin, Junjie Xie, Yu Qin, Zixiang Ding, Zhichao Feng, Xiang Li, Wei Lin
The analysis and mining of user heterogeneous behavior are of paramount importance in recommendation systems.
1 code implementation • 10 Apr 2023 • Zengzhi Wang, Qiming Xie, Yi Feng, Zixiang Ding, Zinong Yang, Rui Xia
Recently, ChatGPT has drawn great attention from both the research community and the public.
no code implementations • 26 Nov 2022 • Zixiang Ding, Guoqing Jiang, Shuai Zhang, Lin Guo, Wei Lin
In this paper, we propose Stochastic Knowledge Distillation (SKD) to obtain compact BERT-style language model dubbed SKDBERT.
1 code implementation • 13 Feb 2022 • Nannan Li, Yaran Chen, Weifan Li, Zixiang Ding, Dongbin Zhao
In this paper, we propose the broad attention to improve the performance by incorporating the attention relationship of different layers for vision transformer, which is called BViT.
no code implementations • 15 Nov 2021 • Zixiang Ding, Yaran Chen, Nannan Li, Dongbin Zhao, C. L. Philip Chen
Moreover, multi-scale feature fusion and knowledge embedding are proposed to improve the performance of BCNN with shallow topology.
no code implementations • 15 Oct 2021 • Fanfan Wang, Zixiang Ding, Rui Xia, Zhaoyu Li, Jianfei Yu
It is also interesting to discover emotions and their causes in conversations.
1 code implementation • 8 Oct 2021 • Jiaqi Li, Haoran Li, Yaran Chen, Zixiang Ding, Nannan Li, Mingjun Ma, Zicheng Duan, Dongbing Zhao
Compared with the traditional rule-based pruning method, this pipeline saves human labor and achieves a higher compression ratio with lower accuracy loss.
no code implementations • 22 Sep 2020 • Nannan Li, Yu Pan, Yaran Chen, Zixiang Ding, Dongbin Zhao, Zenglin Xu
Interestingly, we discover that part of the rank elements is sensitive and usually aggregate in a narrow region, namely an interest region.
no code implementations • 18 Sep 2020 • Zixiang Ding, Yaran Chen, Nannan Li, Dongbin Zhao
For this consequent issue, two solutions are given: 1) we propose Confident Learning Rate (CLR) that considers the confidence of gradient for architecture weights update, increasing with the training time of over-parameterized BCNN; 2) we introduce the combination of partial channel connections and edge normalization that also can improve the memory efficiency further.
1 code implementation • ACL 2020 • Zixiang Ding, Rui Xia, Jianfei Yu
In recent years, a new interesting task, called emotion-cause pair extraction (ECPE), has emerged in the area of text emotion analysis.
Ranked #8 on Emotion-Cause Pair Extraction on ECPE
no code implementations • 18 Jan 2020 • Zixiang Ding, Yaran Chen, Nannan Li, Dongbin Zhao, Zhiquan Sun, C. L. Philip Chen
In this paper, we propose Broad Neural Architecture Search (BNAS) where we elaborately design broad scalable architecture dubbed Broad Convolutional Neural Network (BCNN) to solve the above issue.
3 code implementations • 4 Jun 2019 • Rui Xia, Mengran Zhang, Zixiang Ding
The emotion cause extraction (ECE) task aims at discovering the potential causes behind a certain emotion expression in a document.
Ranked #6 on Emotion Cause Extraction on ECE
1 code implementation • 4 Jun 2019 • Zixiang Ding, Huihui He, Mengran Zhang, Rui Xia
We introduce a relative position augmented embedding learning algorithm, and transform the task from an independent prediction problem to a reordered prediction problem, where the dynamic global label information is incorporated.
Ranked #7 on Emotion Cause Extraction on ECE
4 code implementations • ACL 2019 • Rui Xia, Zixiang Ding
Emotion cause extraction (ECE), the task aimed at extracting the potential causes behind certain emotions in text, has gained much attention in recent years due to its wide applications.
Ranked #13 on Emotion-Cause Pair Extraction on ECPE
2 code implementations • 3 Feb 2018 • Zixiang Ding, Rui Xia, Jianfei Yu, Xiang Li, Jian Yang
Deep neural networks have recently been shown to achieve highly competitive performance in many computer vision tasks due to their abilities of exploring in a much larger hypothesis space.