no code implementations • EMNLP 2020 • Ming Wang, Yinglin Wang
Contextual embeddings are proved to be overwhelmingly effective to the task of Word Sense Disambiguation (WSD) compared with other sense representation techniques.
no code implementations • EMNLP 2021 • Ming Wang, Jianzhang Zhang, Yinglin Wang
In previous similarity-based WSD systems, studies have allocated much effort on learning comprehensive sense embeddings using contextual representations and knowledge sources.
1 code implementation • 23 Mar 2024 • Huaiwen Zhang, Yu Chen, Ming Wang, Shi Feng
The model meticulously considers var-ious evaluative aspects of ESC to apply a more comprehensive and accurate evaluation method for ESC.
1 code implementation • 17 Mar 2024 • Zihan Wang, Fanheng Kong, Shi Feng, Ming Wang, Han Zhao, Daling Wang, Yifei Zhang
Furthermore, we conduct extensive experiments to delve deeper into the potential of Mamba compared to the Transformer in the TSF.
1 code implementation • 26 Feb 2024 • Ming Wang, Yuanzhong Liu, XiaoMing Zhang, Songlian Li, YiJie Huang, Chi Zhang, Daling Wang, Shi Feng, Jigang Li
We have built a community on LangGPT to facilitate the tuition and sharing of prompt design.
1 code implementation • 13 Oct 2023 • Xiaocui Yang, Wenfang Wu, Shi Feng, Ming Wang, Daling Wang, Yang Li, Qi Sun, Yifei Zhang, XiaoMing Fu, Soujanya Poria
Consequently, our work complements research on the performance of MLLMs in multimodal comprehension tasks, achieving a more comprehensive and holistic evaluation of MLLMs.
2 code implementations • 28 Sep 2023 • Ming Wang, Daling Wang, Wenfang Wu, Shi Feng, Yifei Zhang
The application of CEs encounters two main challenges: general user preferences and variable ML systems.
1 code implementation • 29 Jul 2023 • Ming Wang, Wenfang Wu, Chongyun Gao, Daling Wang, Shi Feng, Yifei Zhang
Large language models (LLMs) have received increasing attention.
no code implementations • 25 Jun 2023 • Yuchen Zhuang, Xin Shen, Yan Zhao, Chaosheng Dong, Ming Wang, Jin Li, Chao Zhang
The detection of the underlying shopping intentions of users based on their historical interactions is a crucial aspect for e-commerce platforms, such as Amazon, to enhance the convenience and efficiency of their customers' shopping experiences.
1 code implementation • 23 May 2023 • Jiacheng Li, Ming Wang, Jin Li, Jinmiao Fu, Xin Shen, Jingbo Shang, Julian McAuley
In this paper, we propose to model user preferences and item features as language representations that can be generalized to new items and datasets.
no code implementations • 25 Apr 2023 • Yang Li, Wei Wang, Ming Wang, Chunmeng Dou, Zhengyu Ma, Huihui Zhou, Peng Zhang, Nicola Lepri, Xumeng Zhang, Qing Luo, Xiaoxin Xu, Guanhua Yang, Feng Zhang, Ling Li, Daniele Ielmini, Ming Liu
We propose a binary stochastic learning algorithm that modifies all elementary neural network operations, by introducing (i) stochastic binarization of both the forwarding signals and the activation function derivatives, (ii) signed binarization of the backpropagating errors, and (iii) step-wised weight updates.
no code implementations • ICCV 2023 • Ming Wang, Xianda Guo, Beibei Lin, Tian Yang, Zheng Zhu, Lincheng Li, Shunli Zhang, Xin Yu
This is the first framework on gait recognition that is designed to focus on the extraction of dynamic features.
no code implementations • 15 Nov 2022 • Beibei Lin, Chen Liu, Ming Wang, Lincheng Li, Shunli Zhang, Robby T. Tan, Xin Yu
Existing gait recognition frameworks retrieve an identity in the gallery based on the distance between a probe sample and the identities in the gallery.
no code implementations • 10 Sep 2022 • Cheng Ge, Xi Chen, Ming Wang, Jin Wang
By using this deep network, we can easily locate the baseline position and then provide reliable and interpretable anomaly detection result.
2 code implementations • 2 Aug 2022 • Beibei Lin, Shunli Zhang, Ming Wang, Lincheng Li, Xin Yu
GFR extractor aims to extract contextual information, e. g., the relationship among various body parts, and the mask-based LFR extractor is presented to exploit the detailed posture changes of local regions.
1 code implementation • 30 Apr 2022 • Chengyu Wang, Minghui Qiu, Chen Shi, Taolin Zhang, Tingting Liu, Lei LI, Jianing Wang, Ming Wang, Jun Huang, Wei Lin
The success of Pre-Trained Models (PTMs) has reshaped the development of Natural Language Processing (NLP).
1 code implementation • 8 Mar 2022 • Ming Wang, Beibei Lin, Xianda Guo, Lincheng Li, Zheng Zhu, Jiande Sun, Shunli Zhang, Xin Yu
ECM consists of the Spatial-Temporal feature extractor (ST), the Frame-Level feature extractor (FL) and SPB, and has two obvious advantages: First, each branch focuses on a specific representation, which can be used to improve the robustness of the network.
no code implementations • 24 Nov 2021 • Riya Tyagi, Tanish Tyagi, Ming Wang, Lujin Zhang
Parkinson's disease (PD) is debilitating, progressive, and clinically marked by motor symptoms.
1 code implementation • ACL 2021 • Ming Wang, Yinglin Wang
Lately proposed Word Sense Disambiguation (WSD) systems have approached the estimated upper bound of the task on standard evaluation benchmarks.
no code implementations • 29 Feb 2020 • Yinglin Wang, Ming Wang, Hamido Fujita
Word Sense Disambiguation (WSD) has been a basic and on-going issue since its introduction in natural language processing (NLP) community.
Ranked #1 on Word Sense Disambiguation on Knowledge-based:
no code implementations • SEMEVAL 2017 • Ming Wang, Biao Chu, Qingxun Liu, Xiaobing Zhou
Sentiment analysis is one of the central issues in Natural Language Processing and has become more and more important in many fields.