Yequan Wang (王业全)

Researcher, Team Leader

Beijing Academy of Artificial Intelligence (BAAI)

Biography

Dr. Wang Yequan is a researcher at Beijing Academy of Artificial Intelligence (BAAI). His research interests mainly include large models. He leads the Recognition Team, aiming to build lower-cost but more powerful large models and conduct relevant large model research, eventually achieving the goal of AGI.

Dr. Wang received Ph.D. in Computer Science from Tsinghua University, supervised by Prof. Xiaoyan Zhu and Minlie Huang. From Sep. 2017 to Sep. 2018, Dr. Wang studied at Nanyang Technological University as a Joint Ph.D. Candidate, supervised by Associate Prof. Aixin Sun, who is also the Assistant Chair (Academic).

Dr. Wang has been recognized as the 2022 AI 2000 Most Influential Scholar Honorable Mention in Natural Language Processing.

Team Philosophy for Large Models：Both system capabilities and research capabilities are essential. Without system capabilities, it is not possible to develop large models. Without research capabilities, one can only follow in the footsteps of others. In the case where the leader in large model development chooses to close the source code, it becomes impossible to make further breakthroughs.

Google Scholar Citations: 3,000+

ORCID: 0000-0001-7530-6125

Interests

Large Model
Emboddied AI
Natural Language Processing

Education

PhD in Artificial Intelligence, 2014-2019

Tsinghua University, Beijing
Joint PhD Program, 2017-2018

Nanyang Technological University, Singapore

Projects

FLM Family

The FLM series of large models are a set of large models completed by the Cofe-AI team, including FLM-2, FLM-101B, and FreeLM. The core technology mainly consists of growth techniques, loss prediction technologies, and the FreeLM framework, among others.

Arabic Language Model (ALM 1.0)

We develop and open Arabic Language Model (ALM).

Implicit Sentiment Analysis on Complicated Web Text

National Science Foundation of China (NSFC, 62106249)

Selected Publications

CORT: A New Baseline for Comparative Opinion Classification by Dual Prompts

We approach comparative opinion classification through prompt learning, taking the advantage of embedded knowledge in pre-trained language model. We design a twin framework with dual prompts, named CORT. This extremely simple model delivers state-of-the-art and robust performance on all benchmark datasets for comparative opinion classification. We believe CORT well serves as a new baseline for comparative opinion classification.

Yequan Wang, Hengran Zhang, Aixin Sun, Xuying Meng

CORT: A New Baseline for Comparative Opinion Classification by Dual Prompts

CofeNet: Context and Former-Label Enhanced Net for Complicated Quotation Extraction

We propose the Context and Former-Label Enhanced Net (CofeNet) for quotation extraction. CofeNet is able to extract complicated quotations with components of variable lengths and complicated structures. On two public datasets and one proprietary dataset, we show that our achieves state-of-the-art performance on complicated quotation extraction.

Yequan Wang, Xiang Li, Aixin Sun, Xuying Meng, Huaming Liao, Jiafeng Guo

CofeNet: Context and Former-Label Enhanced Net for Complicated Quotation Extraction

Packet Representation Learning for Traffic Classification

We propose a novel framework to tackle the problem of packet representation learning for various traffic classification tasks. We learn packet representation, preserving both semantic and byte patterns of each packet, and utilize contrastive loss with a sample selector to optimize the learned representations so that similar packets are closer in the latent semantic space.

Xuying Meng, Yequan Wang, Runxin Ma, Haitong Luo, Xiang Li, Yujun Zhang

A Dual-Channel Framework for Sarcasm Recognition by Detecting Sentiment Conflict

In this paper, we propose a Dual-Channel Framework by modeling both literal and implied sentiments separately. Based on this dual-channel framework, we design the Dual-Channel Network (DC-Net) to recognize sentiment conflict.

Yiyi Liu, Yequan Wang, Aixin Sun, Xuying Meng, Jing Li, Jiafeng Guo

A Dual-Channel Framework for Sarcasm Recognition by Detecting Sentiment Conflict

Aspect-level Sentiment Analysis using AS-Capsules

In this paper, we propose the aspect-level sentiment capsules model (AS-Capsules), which is capable of performing aspect detection and sentiment classification simultaneously, in a joint manner.

Yequan Wang, Aixin Sun, Minlie Huang, Xiaoyan Zhu

Aspect-level Sentiment Analysis using AS-Capsules

Sentiment Analysis by Capsules

In this paper, we propose RNN-Capsule, a capsule model based on Recurrent Neural Network (RNN) for sentiment analysis.

Yequan Wang, Aixin Sun, Jialong Han, Ying Liu, Xiaoyan Zhu

Attention-based LSTM for Aspect-level Sentiment Classification

To reveal that the sentiment polarity of a sentence is not only determined by the content but is also highly related to the concerned aspect, we propose an Attention-based Long Short-Term Memory Network for aspect-level sentiment classification.

Yequan Wang, Minlie Huang, Li Zhao, Xiaoyan Zhu

Attention-based LSTM for Aspect-level Sentiment Classification

Recent Publications

Yequan Wang, Hengran Zhang, Aixin Sun, Xuying Meng (2022). CORT: A New Baseline for Comparative Opinion Classification by Dual Prompts. In Findings of the EMNLP 2022.

PDF Cite Code Dataset Project

Ting Lin, Aixin Sun, Yequan Wang (2022). EDU-Capsule: aspect-based sentiment analysis at clause level. KAIS.

PDF Cite Code Slides DOI

Yequan Wang, Xiang Li, Aixin Sun, Xuying Meng, Huaming Liao, Jiafeng Guo (2022). CofeNet: Context and Former-Label Enhanced Net for Complicated Quotation Extraction. In COLING 2022.

PDF Cite Code Dataset Project

Xuying Meng, Yequan Wang, Runxin Ma, Haitong Luo, Xiang Li, Yujun Zhang (2022). Packet Representation Learning for Traffic Classification. In KDD 2022.

PDF Cite Code DOI

Siqi Fan, Yequan Wang, Jing Li, Zheng Zhang, Shuo Shang, Peng Han (2022). Interactive Information Extraction by Semantic Information Graph. In IJCAI 2022.

PDF Cite Code Dataset