no code implementations • 29 Jun 2023 • Yu Tian, Bofang Li, Si Chen, Xubin Li, Hongbo Deng, Jian Xu, Bo Zheng, Qian Wang, Chenliang Li
Recently, Multi-Scenario Learning (MSL) is widely used in recommendation and retrieval systems in the industry because it facilitates transfer learning from different scenarios, mitigating data sparsity and reducing maintenance cost.
no code implementations • 17 Aug 2022 • Shengyu Zhang, Bofang Li, Dong Yao, Fuli Feng, Jieming Zhu, Wenyan Fan, Zhou Zhao, Xiaofei He, Tat-Seng Chua, Fei Wu
Micro-video recommender systems suffer from the ubiquitous noises in users' behaviors, which might render the learned user representation indiscriminating, and lead to trivial recommendations (e. g., popular items) or even weird ones that are far beyond users' interests.
1 code implementation • 13 Jan 2020 • Daoyuan Chen, Yaliang Li, Minghui Qiu, Zhen Wang, Bofang Li, Bolin Ding, Hongbo Deng, Jun Huang, Wei. Lin, Jingren Zhou
Motivated by the necessity and benefits of task-oriented BERT compression, we propose a novel compression method, AdaBERT, that leverages differentiable Neural Architecture Search to automatically compress BERT into task-adaptive small models for specific tasks.
no code implementations • WS 2018 • Marzena Karpinska, Bofang Li, Anna Rogers, Aleks Drozd, R
Languages with logographic writing systems present a difficulty for traditional character-level models.
no code implementations • WS 2018 • Bofang Li, Aleks Drozd, R, Tao Liu, Xiaoyong Du
Subword-level information is crucial for capturing the meaning and morphology of words, especially for out-of-vocabulary entries.
no code implementations • EMNLP 2017 • Bofang Li, Tao Liu, Zhe Zhao, Buzhou Tang, Aleks Drozd, R, Anna Rogers, Xiaoyong Du
The number of word embedding models is growing every year.
no code implementations • EMNLP 2017 • Zhe Zhao, Tao Liu, Shen Li, Bofang Li, Xiaoyong Du
The existing word representation methods mostly limit their information source to word co-occurrence statistics.
no code implementations • SEMEVAL 2017 • Anna Rogers, Aleks Drozd, R, Bofang Li
This paper explores the possibilities of analogical reasoning with vector space models.
1 code implementation • COLING 2016 • Bofang Li, Zhe Zhao, Tao Liu, Puwei Wang, Xiaoyong Du
We train n-gram embeddings and use NB weighting to guide the neural models to focus on important words.
1 code implementation • 27 Dec 2015 • Bofang Li, Tao Liu, Xiaoyong Du, Deyuan Zhang, Zhe Zhao
Many document embeddings methods have been proposed to capture semantics, but they still can't outperform bag-of-ngram based methods on this task.