69 citations · 220 across the 37 of their papers we have counts for
38 papers
Traffic State Prediction and Control Using a Koopman Operator Framework with Polynomial Observables
Shakib Mustavee, Arvind Singh, Shaurya Agarwal
In this paper, we develop a Koopman operator framework for modeling and control of traffic flow governed by the Lighthill--Whitham--Richards (LWR) conservation law. We show that th…
Pre-training with Large Language Model-based Document Expansion for Dense Passage Retrieval
Guangyuan Ma, Xing Wu, Peng Wang +2
In this paper, we systematically study the potential of pre-training with Large Language Model(LLM)-based document expansion for dense passage retrieval. Concretely, we leverage th…
UCAS-IIE-NLP at SemEval-2023 Task 12: Enhancing Generalization of Multilingual BERT for Low-resource Sentiment Analysis
Dou Hu, Lingwei Wei, Yaxin Liu +2
This paper describes our system designed for SemEval-2023 Task 12: Sentiment analysis for African languages. The challenge faced by this task is the scarcity of labeled data and li…
CoT-MoTE: Exploring ConTextual Masked Auto-Encoder Pre-training with Mixture-of-Textual-Experts for Passage Retrieval
Guangyuan Ma, Xing Wu, Peng Wang +1
Passage retrieval aims to retrieve relevant passages from large collections of the open-domain corpus. Contextual Masked Auto-Encoding has been proven effective in representation b…
CoT-MAE v2: Contextual Masked Auto-Encoder with Multi-view Modeling for Passage Retrieval
Xing Wu, Guangyuan Ma, Peng Wang +4
Growing techniques have been emerging to improve the performance of passage retrieval. As an effective representation bottleneck pretraining technique, the contextual masked auto-e…
PUNR: Pre-training with User Behavior Modeling for News Recommendation
Guangyuan Ma, Hongtao Liu, Xing Wu +4
News recommendation aims to predict click behaviors based on user behaviors. How to effectively model the user representations is the key to recommending preferred news. Existing w…
Query-as-context Pre-training for Dense Passage Retrieval
Xing Wu, Guangyuan Ma, Wanhui Qian +2
Recently, methods have been developed to improve the performance of dense passage retrieval by using context-supervised pre-training. These methods simply consider two passages fro…
Supervised Prototypical Contrastive Learning for Emotion Recognition in Conversation
Xiaohui Song, Longtao Huang, Hui Xue +1
Capturing emotions within a conversation plays an essential role in modern dialogue systems. However, the weak correlation between emotions and semantics brings many challenges to…
InfoCSE: Information-aggregated Contrastive Learning of Sentence Embeddings
Xing Wu, Chaochen Gao, Zijia Lin +3
Contrastive learning has been extensively studied in sentence embedding learning, which assumes that the embeddings of different views of the same sentence are closer. The constrai…
RaP: Redundancy-aware Video-language Pre-training for Text-Video Retrieval
Xing Wu, Chaochen Gao, Zijia Lin +3
Video language pre-training methods have mainly adopted sparse sampling techniques to alleviate the temporal redundancy of videos. Though effective, sparse sampling still suffers i…
ConTextual Masked Auto-Encoder for Dense Passage Retrieval
Xing Wu, Guangyuan Ma, Meng Lin +3
Dense passage retrieval aims to retrieve the relevant passages of a query from a large corpus based on dense representations (i.e., vectors) of the query and the passages. Recent s…
Multi-Granularity Semantic Aware Graph Model for Reducing Position Bias in Emotion-Cause Pair Extraction
Yinan Bao, Qianwen Ma, Lingwei Wei +2
The Emotion-Cause Pair Extraction (ECPE) task aims to extract emotions and causes as pairs from documents. We observe that the relative distance distribution of emotions and causes…
HELoC: Hierarchical Contrastive Learning of Source Code Representation
Xiao Wang, Qiong Wu, Hongyu Zhang +5
Abstract syntax trees (ASTs) play a crucial role in source code representation. However, due to the large number of nodes in an AST and the typically deep AST hierarchy, it is chal…
Text Smoothing: Enhance Various Data Augmentation Methods on Text Classification Tasks
Xing Wu, Chaochen Gao, Meng Lin +3
Before entering the neural network, a token is generally converted to the corresponding one-hot representation, which is a discrete distribution of the vocabulary. Smoothed represe…
DistilCSE: Effective Knowledge Distillation For Contrastive Sentence Embeddings
Chaochen Gao, Xing Wu, Peng Wang +4
Large-scale contrastive learning models can learn very informative sentence embeddings, but are hard to serve online due to the huge model size. Therefore, they often play the role…
ESimCSE: Enhanced Sample Building Method for Contrastive Learning of Unsupervised Sentence Embedding
Xing Wu, Chaochen Gao, Liangjun Zang +3
Contrastive learning has been attracting much attention for learning unsupervised sentence embeddings. The current state-of-the-art unsupervised method is the unsupervised SimCSE (…
Smoothed Contrastive Learning for Unsupervised Sentence Embedding
Xing Wu, Chaochen Gao, Yipeng Su +3
Contrastive learning has been gradually applied to learn high-quality unsupervised sentence embedding. Among the previous un-supervised methods, the latest state-of-the-art method,…
Towards Propagation Uncertainty: Edge-enhanced Bayesian Graph Convolutional Networks for Rumor Detection
Lingwei Wei, Dou Hu, Wei Zhou +2
Detecting rumors on social media is a very critical task with significant implications to the economy, public health, etc. Previous works generally capture effective features from…
PEN4Rec: Preference Evolution Networks for Session-based Recommendation
Dou Hu, Lingwei Wei, Wei Zhou +3
Session-based recommendation aims to predict user the next action based on historical behaviors in an anonymous session. For better recommendations, it is vital to capture user pre…
SRLF: A Stance-aware Reinforcement Learning Framework for Content-based Rumor Detection on Social Media
Chunyuan Yuan, Wanhui Qian, Qianwen Ma +2
The rapid development of social media changes the lifestyle of people and simultaneously provides an ideal place for publishing and disseminating rumors, which severely exacerbates…
Embedding API Dependency Graph for Neural Code Generation
Chen Lyu, Ruyun Wang, Hongyu Zhang +2
The problem of code generation from textual program descriptions has long been viewed as a grand challenge in software engineering. In recent years, many deep learning based approa…
Early Detection of Fake News by Utilizing the Credibility of News, Publishers, and Users Based on Weakly Supervised Learning
Chunyuan Yuan, Qianwen Ma, Wei Zhou +2
The dissemination of fake news significantly affects personal reputation and public trust. Recently, fake news detection has attracted tremendous attention, and previous studies ma…
Hierarchical Interaction Networks with Rethinking Mechanism for Document-level Sentiment Analysis
Lingwei Wei, Dou Hu, Wei Zhou +5
Document-level Sentiment Analysis (DSA) is more challenging due to vague semantic links and complicate sentiment information. Recent works have been devoted to leveraging text summ…
DyHGCN: A Dynamic Heterogeneous Graph Convolutional Network to Learn Users' Dynamic Preferences for Information Diffusion Prediction
Chunyuan Yuan, Jiacheng Li, Wei Zhou +3
Information diffusion prediction is a fundamental task for understanding the information propagation process. It has wide applications in such as misinformation spreading predictio…