no code implementations • 19 Oct 2022 • Shixuan Zhu, Qi Shen, Yiming Zhang, Zhenwei Dong, Zhihua Wei
In this paper, we propose a novel graph learning paradigm called Counterfactual Learning for Bundle Recommendation (CLBR) to mitigate the impact of data sparsity problem and improve bundle recommendation.
1 code implementation • COLING 2022 • Xinpeng Wang, Han Jiang, Zhihua Wei, Shanlin Zhou
Story generation has emerged as an interesting yet challenging NLP task in recent years.
1 code implementation • 29 Mar 2022 • Rui Wang, Qibing Bai, Junyi Ao, Long Zhou, Zhixiang Xiong, Zhihua Wei, Yu Zhang, Tom Ko, Haizhou Li
LightHuBERT outperforms the original HuBERT on ASR and five SUPERB tasks with the HuBERT size, achieves comparable performance to the teacher model in most tasks with a reduction of 29% parameters, and obtains a $3. 5\times$ compression ratio in three SUPERB tasks, e. g., automatic speaker verification, keyword spotting, and intent classification, with a slight accuracy loss.
Automatic Speech Recognition Automatic Speech Recognition (ASR) +6
no code implementations • 31 Dec 2021 • Qi Shen, Shixuan Zhu, Yitong Pang, Yiming Zhang, Zhihua Wei
Session-based recommendation (SBR) is a challenging task, which aims at recommending next items based on anonymous interaction sequences.
1 code implementation • 31 Dec 2021 • Chuan Cui, Qi Shen, Shixuan Zhu, Yitong Pang, Yiming Zhang, Hanning Gao, Zhihua Wei
Session-based recommendation (SBR) is proposed to recommend items within short sessions given that user profiles are invisible in various scenarios nowadays, such as e-commerce and short video recommendation.
1 code implementation • 22 Dec 2021 • Yiming Zhang, Lingfei Wu, Qi Shen, Yitong Pang, Zhihua Wei, Fangli Xu, Bo Long, Jian Pei
As a result, we first propose a more realistic CRS learning setting, namely Multi-Interest Multi-round Conversational Recommendation, where users may have multiple interests in attribute instance combinations and accept multiple items with partially overlapped combinations of attribute instances.
no code implementations • 20 Nov 2021 • Hanning Gao, Lingfei Wu, Po Hu, Zhihua Wei, Fangli Xu, Bo Long
Finally, we apply an answer selection model on the full KSG and the top-ranked sub-KSGs respectively to validate the effectiveness of our proposed graph-augmented learning to rank method.
no code implementations • 20 Nov 2021 • Hanning Gao, Lingfei Wu, Hongyun Zhang, Zhihua Wei, Po Hu, Fangli Xu, Bo Long
Most previous methods solve this task using a sequence-to-sequence model or using a graph-based model to encode RDF triples and to generate a text sequence.
2 code implementations • ACL 2022 • Junyi Ao, Rui Wang, Long Zhou, Chengyi Wang, Shuo Ren, Yu Wu, Shujie Liu, Tom Ko, Qing Li, Yu Zhang, Zhihua Wei, Yao Qian, Jinyu Li, Furu Wei
Motivated by the success of T5 (Text-To-Text Transfer Transformer) in pre-trained natural language processing models, we propose a unified-modal SpeechT5 framework that explores the encoder-decoder pre-training for self-supervised speech/text representation learning.
Automatic Speech Recognition Automatic Speech Recognition (ASR) +7
no code implementations • 11 Oct 2021 • Rui Wang, Junyi Ao, Long Zhou, Shujie Liu, Zhihua Wei, Tom Ko, Qing Li, Yu Zhang
In this work, we propose a novel multi-view self-attention mechanism and present an empirical study of different Transformer variants with or without the proposed attention mechanism for speaker recognition.
no code implementations • 24 Sep 2021 • Yiming Zhang, Lingfei Wu, Qi Shen, Yitong Pang, Zhihua Wei, Fangli Xu, Ethan Chang, Bo Long
In this work, we propose an end-to-end heterogeneous global graph learning framework, namely Graph Learning Augmented Heterogeneous Graph Neural Network (GL-HGNN) for social recommendation.
no code implementations • 24 Sep 2021 • Qi Shen, Lingfei Wu, Yitong Pang, Yiming Zhang, Zhihua Wei, Fangli Xu, Bo Long
Based on the global graph, MGCNet attaches the global interest representation to final item representation based on local contextual intention to address the limitation (iii).
1 code implementation • 9 Jul 2021 • Wen Shen, Zhihua Wei, Shikun Huang, BinBin Zhang, Jiaqi Fan, Ping Zhao, Quanshi Zhang
The reasonable definition of semantic interpretability presents the core challenge in explainable AI.
1 code implementation • 8 Jul 2021 • Yitong Pang, Lingfei Wu, Qi Shen, Yiming Zhang, Zhihua Wei, Fangli Xu, Ethan Chang, Bo Long, Jian Pei
Additionally, existing personalized session-based recommenders capture user preference only based on the sessions of the current user, but ignore the useful item-transition patterns from other user's historical sessions.
1 code implementation • 25 Mar 2021 • Rui Wang, Zhihua Wei, Haoran Duan, Shouling Ji, Yang Long, Zhen Hong
Compared with hand-designed approaches, neural architecture search (NAS) appears as a practical technique in automating the manual architecture design process and has attracted increasing interest in spoken language processing tasks such as speaker recognition.
no code implementations • 20 Oct 2020 • Shufan Shen, Ran Miao, Yi Wang, Zhihua Wei
In this report, we discribe the submission of Tongji University undergraduate team to the CLOSE track of the VoxCeleb Speaker Recognition Challenge (VoxSRC) 2020 at Interspeech 2020.
1 code implementation • ECCV 2020 • Wen Shen, BinBin Zhang, Shikun Huang, Zhihua Wei, Quanshi Zhang
This paper proposes a set of rules to revise various neural networks for 3D point cloud processing to rotation-equivariant quaternion neural networks (REQNNs).
1 code implementation • CVPR 2021 • Wen Shen, Zhihua Wei, Shikun Huang, BinBin Zhang, Panyue Chen, Ping Zhao, Quanshi Zhang
In this paper, we diagnose deep neural networks for 3D point cloud processing to explore utilities of different intermediate-layer network architectures.