13 citations · 75 across the 17 of their papers we have counts for
17 papers
AAKT: Enhancing Knowledge Tracing with Alternate Autoregressive Modeling
Hao Zhou, Wenge Rong, Jianfei Zhang +3
Knowledge Tracing (KT) aims to predict students' future performances based on their former exercises and additional information in educational settings. KT has received significant…
Explainable Few-shot Knowledge Tracing
Haoxuan Li, Jifan Yu, Yuanxin Ouyang +4
Knowledge tracing (KT), aiming to mine students' mastery of knowledge by their exercise records and predict their performance on future test questions, is a critical task in educat…
Wasserstein Dependent Graph Attention Network for Collaborative Filtering with Uncertainty
Haoxuan Li, Yuanxin Ouyang, Zhuang Liu +2
Collaborative filtering (CF) is an essential technique in recommender systems that provides personalized recommendations by only leveraging user-item interactions. However, most CF…
ProCQA: A Large-scale Community-based Programming Question Answering Dataset for Code Search
Zehan Li, Jianfei Zhang, Chuantao Yin +2
Retrieval-based code question answering seeks to match user queries in natural language to relevant code snippets. Previous approaches typically rely on pretraining models using cr…
A Review of Data Mining in Personalized Education: Current Trends and Future Prospects
Zhang Xiong, Haoxuan Li, Zhuang Liu +4
Personalized education, tailored to individual student needs, leverages educational technology and artificial intelligence (AI) in the digital age to enhance learning effectiveness…
Discovering Sounding Objects by Audio Queries for Audio Visual Segmentation
Shaofei Huang, Han Li, Yuqing Wang +5
Audio visual segmentation (AVS) aims to segment the sounding objects for each frame of a given video. To distinguish the sounding objects from silent ones, both audio-visual semant…
Knowledge-Driven CoT: Exploring Faithful Reasoning in LLMs for Knowledge-intensive Question Answering
Keheng Wang, Feiyu Duan, Sirui Wang +5
Equipped with Chain-of-Thought (CoT), Large language models (LLMs) have shown impressive reasoning ability in various downstream tasks. Even so, suffering from hallucinations and t…
Evaluating Open-Domain Dialogues in Latent Space with Next Sentence Prediction and Mutual Information
Kun Zhao, Bohao Yang, Chenghua Lin +3
The long-standing one-to-many issue of the open-domain dialogues poses significant challenges for automatic evaluation methods, i.e., there may be multiple suitable responses which…
Transformer-Patcher: One Mistake worth One Neuron
Zeyu Huang, Yikang Shen, Xiaofeng Zhang +3
Large Transformer-based Pretrained Language Models (PLMs) dominate almost all Natural Language Processing (NLP) tasks. Nevertheless, they still make mistakes from time to time. For…
Improving Variational Autoencoders with Density Gap-based Regularization
Jianfei Zhang, Jun Bai, Chenghua Lin +2
Variational autoencoders (VAEs) are one of the powerful unsupervised learning frameworks in NLP for latent representation learning and latent-directed generation. The classic optim…
Mixture of Attention Heads: Selecting Attention Heads Per Token
Xiaofeng Zhang, Yikang Shen, Zeyu Huang +3
Mixture-of-Experts (MoE) networks have been proposed as an efficient way to scale up model capacity and implement conditional computing. However, the study of MoE components mostly…
Entity Aware Syntax Tree Based Data Augmentation for Natural Language Understanding
Jiaxing Xu, Jianbin Cui, Jiangneng Li +2
Understanding the intention of the users and recognizing the semantic entities from their sentences, aka natural language understanding (NLU), is the upstream task of many natural…
Causal Intervention for Fairness in Multi-behavior Recommendation
Xi Wang, Wenjie Wang, Fuli Feng +3
Recommender systems usually learn user interests from various user behaviors, including clicks and post-click behaviors (e.g., like and favorite). However, these behaviors inevitab…
Query-Response Interactions by Multi-tasks in Semantic Search for Chatbot Candidate Retrieval
Libin Shi, Kai Zhang, Wenge Rong
Semantic search for candidate retrieval is an important yet neglected problem in retrieval-based Chatbots, which aims to select a bunch of candidate responses efficiently from a la…
Enhancing Dual-Encoders with Question and Answer Cross-Embeddings for Answer Retrieval
Yanmeng Wang, Jun Bai, Ye Wang +5
Dual-Encoders is a promising mechanism for answer retrieval in question answering (QA) systems. Currently most conventional Dual-Encoders learn the semantic representations of ques…
Why Do Neural Response Generation Models Prefer Universal Replies?
Bowen Wu, Nan Jiang, Zhifeng Gao +6
Recent advances in sequence-to-sequence learning reveal a purely data-driven approach to the response generation task. Despite its diverse applications, existing neural models are…
Word Embedding based Correlation Model for Question/Answer Matching
Yikang Shen, Wenge Rong, Nan Jiang +3
With the development of community based question answering (Q&A) services, a large scale of Q&A archives have been accumulated and are an important information and knowledge resour…