Papers by Zhepeng Lv

4 papers
PUNR: Pre-training with User Behavior Modeling for News Recommendation (2023.findings-emnlp)

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Guangyuan Ma, Hongtao Liu, Xing W, Wanhui Qian, Zhepeng Lv, Qing Yang, Songlin Hu
Semantic Text Processing
Challenge: Existing news recommendation methods use pre-trained language models to produce news vectors and user vectors.
Approach: They propose an unsupervised pre-training paradigm with two tasks for user behavior modeling.
Outcome: The proposed model improves on the real-world news benchmark.
Contrastive Pre-training for Personalized Expert Finding (2023.findings-emnlp)

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Qiyao Peng, Hongtao Liu, Zhepeng Lv, Qing Yang, Wenjun Wang
Semantic Text Processing
Challenge: Existing approaches to expert finding are effective for a community question answering platform.
Approach: They propose a CQA-domain Contrastive Pre-training framework for Expert Finding which could learn more comprehensive question representations.
Outcome: The proposed framework could learn more comprehensive question representations on six real-world datasets.
Adaptive Attention for Sparse-based Long-sequence Transformer (2023.findings-acl)

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Xuanyu Zhang, Zhepeng Lv, Qing Yang
Language Models
Challenge: Recent studies show that Transformers can process longer sequences because of their complexity and time scales quadratic to the sequence length.
Approach: They propose an efficient Transformer model with adaptive attention that can select useful tokens automatically in sparse attention by learnable position vectors.
Outcome: The proposed model can select useful tokens automatically in sparse attention by learnable position vectors.
Pre-trained Personalized Review Summarization with Effective Salience Estimation (2023.findings-acl)

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Hongyan Xu, Hongtao Liu, Zhepeng Lv, Qing Yang, Wenjun Wang
Summarization
Challenge: Pretrained language models (PLMs) are a new paradigm in text generation for the strong ability of natural language comprehension.
Approach: They propose a pre-trained personalized review summarization method that incorporates personalized information into the salience estimation of input reviews.
Outcome: The proposed method performs better than the state-of-the-art methods on real-world datasets.

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