6 citations · 10 across the 8 of their papers we have counts for
8 papers
ML-LMCL: Mutual Learning and Large-Margin Contrastive Learning for Improving ASR Robustness in Spoken Language Understanding
Xuxin Cheng, Bowen Cao, Qichen Ye +3
Spoken language understanding (SLU) is a fundamental task in the task-oriented dialogue systems. However, the inevitable errors from automatic speech recognition (ASR) usually impa…
NADiffuSE: Noise-aware Diffusion-based Model for Speech Enhancement
Wen Wang, Dongchao Yang, Qichen Ye +2
The goal of speech enhancement (SE) is to eliminate the background interference from the noisy speech signal. Generative models such as diffusion models (DM) have been applied to t…
LLMRec: Benchmarking Large Language Models on Recommendation Task
Junling Liu, Chao Liu, Peilin Zhou +8
Recently, the fast development of Large Language Models (LLMs) such as ChatGPT has significantly advanced NLP tasks by enhancing the capabilities of conversational models. However,…
Attention Calibration for Transformer-based Sequential Recommendation
Peilin Zhou, Qichen Ye, Yueqi Xie +5
Transformer-based sequential recommendation (SR) has been booming in recent years, with the self-attention mechanism as its key component. Self-attention has been widely believed t…
FTM: A Frame-level Timeline Modeling Method for Temporal Graph Representation Learning
Bowen Cao, Qichen Ye, Weiyuan Xu +1
Learning representations for graph-structured data is essential for graph analytical tasks. While remarkable progress has been made on static graphs, researches on temporal graphs…
FiTs: Fine-grained Two-stage Training for Knowledge-aware Question Answering
Qichen Ye, Bowen Cao, Nuo Chen +2
Knowledge-aware question answering (KAQA) requires the model to answer questions over a knowledge base, which is essential for both open-domain QA and domain-specific QA, especiall…
Rethinking Multi-Interest Learning for Candidate Matching in Recommender Systems
Yueqi Xie, Jingqi Gao, Peilin Zhou +5
Existing research efforts for multi-interest candidate matching in recommender systems mainly focus on improving model architecture or incorporating additional information, neglect…
Equivariant Contrastive Learning for Sequential Recommendation
Peilin Zhou, Jingqi Gao, Yueqi Xie +5
Contrastive learning (CL) benefits the training of sequential recommendation models with informative self-supervision signals. Existing solutions apply general sequential data augm…