Search Results for author: Wenpeng Lu

Found 14 papers, 4 papers with code

Word Sense Disambiguation with Knowledge-Enhanced and Local Self-Attention-based Extractive Sense Comprehension

1 code implementation COLING 2022 Guobiao Zhang, Wenpeng Lu, Xueping Peng, Shoujin Wang, Baoshuo Kan, Rui Yu

Word sense disambiguation (WSD), identifying the most suitable meaning of ambiguous words in the given contexts according to a predefined sense inventory, is one of the most classical and challenging tasks in natural language processing.

Sentence Word Sense Disambiguation

An empirical study of next-basket recommendations

no code implementations5 Dec 2023 Zhufeng Shao, Shoujin Wang, Qian Zhang, Wenpeng Lu, Zhao Li, Xueping Peng

This methodological rigor establishes a cohesive framework for the impartial evaluation of diverse NBR approaches.

Recommendation Systems

Knowledge-Aware Prompt Tuning for Generalizable Vision-Language Models

no code implementations ICCV 2023 Baoshuo Kan, Teng Wang, Wenpeng Lu, XianTong Zhen, Weili Guan, Feng Zheng

Pre-trained vision-language models, e. g., CLIP, working with manually designed prompts have demonstrated great capacity of transfer learning.

Few-Shot Image Classification Transfer Learning

Medical Question Summarization with Entity-driven Contrastive Learning

1 code implementation15 Apr 2023 Sibo Wei, Wenpeng Lu, Xueping Peng, Shoujin Wang, Yi-Fei Wang, Weiyu Zhang

Although existing works have attempted to utilize Seq2Seq, reinforcement learning, or contrastive learning to solve the problem, two challenges remain: how to correctly capture question focus to model its semantic intention, and how to obtain reliable datasets to fairly evaluate performance.

Contrastive Learning Question Answering

Tri-Attention: Explicit Context-Aware Attention Mechanism for Natural Language Processing

1 code implementation5 Nov 2022 Rui Yu, Yifeng Li, Wenpeng Lu, Longbing Cao

In natural language processing (NLP), the context of a word or sentence plays an essential role.

Sentence

A Systematical Evaluation for Next-Basket Recommendation Algorithms

no code implementations7 Sep 2022 Zhufeng Shao, Shoujin Wang, Qian Zhang, Wenpeng Lu, Zhao Li, Xueping Peng

Different studies often evaluate NBR approaches on different datasets, under different experimental settings, making it hard to fairly and effectively compare the performance of different NBR approaches.

Next-basket recommendation Recommendation Systems

Modeling Multi-interest News Sequence for News Recommendation

no code implementations15 Jul 2022 Rongyao Wang, Wenpeng Lu

In MINS, a news encoder based on self-attention is devised on learn an informative embedding for each piece of news, and then a novel parallel interest network is devised to extract the potential multiple interests embedded in the news sequence in preparation for the subsequent next-news recommendations.

News Recommendation Recommendation Systems

Modeling Complex Dependencies for Session-based Recommendations via Graph Neural Networks

no code implementations29 Jan 2022 Qian Zhang, Wenpeng Lu

Based on a strong assumption of adjacent dependency, any two adjacent items in a session are necessarily dependent in most GNN-based SBRs.

Representation Learning Session-Based Recommendations

Aspect-driven User Preference and News Representation Learning for News Recommendation

no code implementations12 Oct 2021 Rongyao Wang, Wenpeng Lu, Shoujin Wang, Xueping Peng, Hao Wu, Qian Zhang

News recommender systems are essential for helping users to efficiently and effectively find out those interesting news from a large amount of news.

News Recommendation Recommendation Systems +1

Jointly Modeling Intra- and Inter-transaction Dependencies with Hierarchical Attentive Transaction Embeddings for Next-item Recommendation

no code implementations30 May 2020 Shoujin Wang, Longbing Cao, Liang Hu, Shlomo Berkovsky, Xiaoshui Huang, Lin Xiao, Wenpeng Lu

Most existing TBRSs recommend next item by only modeling the intra-transaction dependency within the current transaction while ignoring inter-transaction dependency with recent transactions that may also affect the next item.

Recommendation Systems

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